Winter weather research in complex terrain during ICE-POP 2018 (International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games)
Link: Atmospheric Chemistry and Physics
(Editor(s): ACP co-editors | Coordinators: Timothy Garrett and Matthias Tesche | Co-organizers: GyuWon Lee, Francisco J. Tapiador, and Zoltan Toth)
Link: Atmospheric Measurement Techniques
(Editor(s): Alexis Berne, Stefan Kneifel, S. Joseph Munchak, GyuWon Lee, Alexander Ryzhkov, Paul Joe, and Francisco J. Tapiador)
Link: Geoscientific Model Development
(Editor(s): GMD topical editors | Coordinator: GyuWon Lee)
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Joe, P., Lee, G., and Kim, K.: The Challenges of Micro-Nowcasting and the Women’s Slope Style Event at the PyeongChang 2018 Olympic Winter Games, Meteorol., 2(1), 107-127, doi:10.3390/meteorology2010008, 2023. Link PDF
Abstract
The Women’s Slope Style event of 11–12 February 2018 at the PyeongChang 2018 Olympic Winter Games posed considerable challenges to the competitors and decision-makers, requiring sub-kilometer and sub-minute weather predictions in complex terrain. The gusty wind conditions were unfair and unsafe as the competitors could not achieve sufficient speed to initiate or complete their jumps. The term micro-nowcasting is used here to reflect the extreme high-resolution nature of these science and service requirements. The World Meteorological Organization has conducted several research development and forecast demonstration projects to advance, accelerate and promote the art of nowcasting. Data from compact automatic weather stations, located along the field of play, reported every minute and were post-processed using time series, Hovmöller and wavelet transforms to succinctly present the information. The analyses revealed dominant frequencies of about 20 min, presumed to be associated with vortex shedding from the mountain ridges, but were unable to directly capture the gusts that affected the competitors. The systemic challenges from this and previous projects are reviewed. They include the lack of adequate scientific knowledge of microscale processes, gaps in modeling, the need for post-processing, forecast techniques, managing ever-changing service requirements and highlights the role of observations and the critical role of the forecaster. These challenges also apply to future high-resolution operational weather and warning services. -
Joshil, S. S., and Chandrasekar, V.: Attenuation Correction in Weather Radars for Snow, IEEE Trans. Geosci. Remote Sens., Early Access, doi:10.1109/TGRS.2023.3254555, 2023. Link PDF
Abstract
Weather radars play a prominent role in remote sensing of the atmosphere. Various fields, such as meteorology and hydrology, rely on accurate weather radar data as input for their models. Different hydrometeors present during a weather event influence the amount of attenuation encountered by the radar signal. Attenuation correction for dual-polarization weather radar data is necessary to improve the radar products and get accurate measurements. Most of the existing attenuation correction research is associated with rain hydrometeors. Currently, research that addresses the attenuation correction of snow in weather radars is limited. Although it is known that attenuation of radar signals when it encounters rain is much greater than that for snow, attenuation for all hydrometeors needs to be addressed for accurate radar estimates. In this research work, the attenuation of different hydrometeors is studied using signal simulations. Various factors which influence attenuation, such as the elevation angle and particle size distribution, are considered, and the results are presented. An attenuation correction algorithm that uses the hydrometeor classification and specific differential phase products from the DROPS2.0 algorithm is introduced. Signal simulations are employed to obtain the relationship between specific attenuation and specific differential phase for different hydrometeors used in the proposed algorithm. The attenuation correction method is applied to X-band and Ku-band radar data. Path integrated attenuation of about 8 dB was observed in the snow case discussed from Ku band radar data. The method proposed for attenuation shows promising results at both frequency bands. -
Joshil, S. S., Chandrasekar, V., and Wolff, D. B.: Overview of the D3R Observations During the ICE-POP Field Campaign With Emphasis on Snow Studies, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 16, 1668-1677, doi:10.1109/JSTARS.2023.3239593, 2023. Link PDF
Abstract
The International Collaborative Experiment during the PyeongChang Olympics and Paralympic winter games 2018 took place in the PyeongChang region of South Korea. The main goal of this field campaign was to study winter precipitation in an environment that has complex terrain. The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was calibrated and deployed in this field campaign. The positioning error of the radar was calibrated to be within 0.1°. The D3R was deployed for more than four months and was able to capture many interesting snowfall events along with a few rain events. In this article, the deployment and performance of the D3R during the campaign are discussed. The snowfall events captured by the D3R are discussed in detail to interpret the microphysics from a radar's perspective. The reflectivity–snowfall rate relationship is derived at the Ku band, and the snow accumulation computed is in good agreement with a precipitation gauge that was deployed near the radar. The benefit of the dual-frequency ratio for identifying the precipitation particle types is briefly introduced using the data from a large snow event on 28th February 2018. The vertical profile D3R data for this snow event are studied for detecting the presence of pristine-oriented ice crystals in the mixed hydrometeor phase conditions. Various other instruments, such as X-band radar and disdrometers, were deployed in the campaign. The D3R data are compared with the MxPOL X-band radar, and the reflectivity values match within a couple of dB in the common volume region. -
Kim, J.-H., Goo, T.-Y., Jung, S.-P., Kim, M.-S., Lee, K., Kang, M., Lee. C., Yang, J., Hong, S., Ko, H., and Yun, J. H.: Overview of the KMA/NIMS Atmospheric Research Aircraft (NARA) and its data archive: Annual airborne observations over the Korean peninsula, Geosci. Data J., 10(4), 447-460, doi:10.1002/gdj3.182, 2023. Link PDF
Abstract
This study describes the Korea Meteorological Administration/National Institute of Meteorological Sciences (KMA/NIMS) Atmospheric Research Aircraft (NARA) and its observational data archive. NARA has been performing annual observation flights, which are aimed at reducing the uncertainty of atmospheric observations in the observation data gap area around the Korean peninsula, since January 2018. An online system has also been constructed to provide data management, transmission, quality control and simple visualization. The mission strategy of NARA is subdivided into four individual units, namely observations of severe weather (SW), climate monitoring (CM), environmental monitoring (EM) and cloud physics and weather modification experiments (CP). As of December 2020, NARA has been launched operationally for 325 flights (corresponding to 9.02 flights per month), typically over the West Sea, mid-inland areas (34.4% and 25.4%, respectively), and below an altitude of 3 km (51.9%). Results of intercomparison tests confirmed that NARA measurements have reasonable offsets (<0.9%) to each other in terms of pressure and temperature. Moreover, the monthly average temperature profile in the East Sea area showed a seasonal variation was detected in monthly variation. From these results, it is evident that NARA data will contribute significantly to enhancing the level of scientific understanding of atmospheric observations and the applications (i.e. long-term study) thereof as the amount of data accumulated increases. -
Kwon, J., Lim, K.-S. S., Park, S.-Y., Kim, K., and Lee, G.: Effects of Prognostic Number Concentrations of Snow and Graupel on the Simulated Precipitation over the Korean Peninsula, Wea. Forecast., in press, doi:10.1175/WAF-D-23-0057.1, 2023. Link
Abstract
A new version of the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme was developed based on the existing WDM6 scheme by predicting snow and graupel number concentrations. The new WDM6 scheme was tested for summer rainfall and winter snowfall cases to evaluate the effects of prognostic number concentration of snow and graupel on the simulated precipitation. The number concentration of snow decreases at the upper layers and one of graupel also decreases at all layers in the new WDM6 scheme compared to the diagnosed ones in the original WDM6 scheme. Rain number concentration is remarkably reduced in the new WDM6 scheme due to the newly added and modified sink processes. Therefore, the new scheme produces a larger size of raindrops with a reduced number concentration than the original scheme, which hinders raindrop evaporation and produces more surface rain. Even though the enhanced surface rainfall in the new scheme deteriorates the bias score, the new scheme improves the statistical skill of the equitable threat score and probability of detection in most cases. These scores all improved for warm-type summer cases in the new scheme. The new scheme also shows more comparable features to the observation for the probability density functions of simulated liquid equivalent precipitation rates by alleviating the overprediction problem of precipitation frequencies belonging to heavy precipitation categories. Therefore, the new scheme improves the precipitation forecast for warm-type summer cases, which occur most frequently during the summer season over the Korean Peninsula. -
Park, S.-Y., and Lim, K.-S. S.: Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme, J. Adv. Model. Earth Syst., 15, e2022MS003009, doi:10.1029/2022MS003009, 2023. Link PDF
Abstract
The ice microphysical processes in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme are treated as a single-moment approach, in which the number concentration of cloud ice is diagnosed based on its mixing ratio. This study develops the revised WDM6 scheme through the implementation of prognostic cloud ice number concentrations. The effect of the prognostic number concentration on the simulated precipitation is verified through simulations of short-term winter snowfall cases during International Collaborative Experiments for the Pyeongchang 2018 Olympics and Paralympics (ICE-POP 2018) winter games and a 1-month regional climate case during the summer season, July 2009. For all cases, the revised WDM6 simulates higher cloud ice number concentrations and lower cloud ice mixing ratios than the original WDM6. The microphysics budget analysis for the snowfall cases shows that the inefficient deposition and vapor freezing nucleation processes of cloud ice reduce the available cloud ice mixing ratio. Consequently, the accretion processes with cloud ice decrease and the deposition into snow increases due to the surplus water vapor. The revised WDM6 alleviates the positive bias of surface precipitation consisting of snow over the region where the original WDM6 simulates excessive precipitation, compared to the observed data. For the regional climate case, the reduced cloud ice amount strengthens the Western North Pacific high-pressure system by allowing more solar radiation to reach the surface, leading to simulated precipitation bands and synoptic environments that are more comparable with the observed data. -
Tokay, A., Helms, C. N., Kim, K., Gatlin, P. N., and Wolff, D. B: Evaluation of SWER(Ze) relationships by precipitation imaging package (PIP) during ICE-POP 2018, J. Hydrometeorol., 24(2), 691–708, doi:10.1175/JHM-D-22-0101.1, 2023. Link
Abstract
Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application. -
Tokay, A., Liao, L., Meneghini, R., Helms, C. N., Munchak, S. J., Wolff, D. B., and Gatlin, P. N.: Retrieval of Normalized Gamma Size Distribution Parameters using Precipitation Imaging Package (PIP) Snowfall Observations during ICE-POP 2018, J. Appl. Meteorol. Climatol., Early Online Release, doi:10.1175/JAMC-D-21-0266.1, 2023. Link
Abstract
Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment - PyeongChang Olympics and Paralympic (ICE-POP 2018). Two of the gamma PSD parameters, the mass weighted particle diameter (Dmass) and the normalized intercept parameter NW, have median values of 1.15-1.31 mm and 2.84-3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, Dmx−Deq, and the relationship between the Reynolds and Best numbers, Re-X. Normalization of snow water equivalent rate (SWER) and ice water content (W) by NW reduces the range in NW resulting in well fitted power law relationship, between SWER/NW and Dmass and between W/NW and Dmass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter (μ) ranging from −2 to 10. NASA's Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and μ = 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23−0.34 when μ = 2 and by 0.29−0.40 when μ = 3, while the MFB of SWER had a much narrower range (−0.03 to 0.04) for the same μ values. -
Tsai, C.-L., Kim, K., Liou, Y.-C., and Lee, G.: High-resolution 3D winds derived from a modified WISSDOM synthesis scheme using multiple Doppler lidars and observations, Atmos. Meas. Tech., 16, 845–869, doi:10.5194/amt-16-845-2023, 2023. Link PDF
Abstract
The WISSDOM (Wind Synthesis System using Doppler Measurements) synthesis scheme was developed to derive high-resolution 3-dimensional (3D) winds under clear-air conditions. From this variational-based scheme, detailed wind information was obtained from scanning Doppler lidars, automatic weather stations (AWSs), sounding observations, and local reanalysis datasets (LDAPS, Local Data Assimilation and Prediction System), which were utilized as constraints to minimize the cost function. The objective of this study is to evaluate the performance and accuracy of derived 3D winds from this modified scheme. A strong wind event was selected to demonstrate its performance over complex terrain in Pyeongchang, South Korea. The size of the test domain is 12×12 km2 extended up to 3 km a.m.s.l. (above mean sea level) height with a remarkably high horizontal and vertical resolution of 50 m. The derived winds reveal that reasonable patterns were explored from a control run, as they have significant similarity with the sounding observations. The results of intercomparisons show that the correlation coefficients between derived horizontal winds and sounding observations are 0.97 and 0.87 for u- and v-component winds, respectively, and the averaged bias (root mean square deviation, RMSD) of horizontal winds is between −0.78 and 0.09 (1.77 and 1.65) m s−1. The correlation coefficients between WISSDOM-derived winds and lidar QVP (quasi-vertical profile) are 0.84 and 0.35 for u- and v-component winds, respectively, and the averaged bias (RMSD) of horizontal winds is between 2.83 and 2.26 (3.69 and 2.92) m s−1. The statistical errors also reveal a satisfying performance of the retrieved 3D winds; the median values of wind directions are −5 to 5 (0 to 2.5)∘, the wind speed is approximately −1 to 3 m s−1 (−1 to 0.5 m s−1), and the vertical velocity is −0.2 to 0.6 m s−1 compared with the lidar QVP (sounding observations). A series of sensitivity tests with different weighting coefficients, radius of influence (RI) in interpolation, and various combination of different datasets were also performed. The results indicate that the present setting of the control run is the optimal reference to WISSDOM synthesis in this event and will help verify the impacts against various scenarios and observational references in this area.
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Grazioli, J., Ghiggi, G., Billault-Roux, A.-C., and Berne, A.: MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall, Sci. Data, 9, 186, doi:10.1038/s41597-022-01269-7, 2022. Link PDF
Abstract
Snowfall information at the scale of individual particles is rare, difficult to gather, but fundamental for a better understanding of solid precipitation microphysics. In this article we present a dataset (with dedicated software) of in-situ measurements of snow particles in free fall. The dataset includes gray-scale (255 shades) images of snowflakes, co-located surface environmental measurements, a large number of geometrical and textural snowflake descriptors as well as the output of previously published retrieval algorithms. These include: hydrometeor classification, riming degree estimation, identification of melting particles, discrimination of wind-blown snow, as well as estimates of snow particle mass and volume. The measurements were collected in various locations of the Alps, Antarctica and Korea for a total of 2’555’091 snowflake images (or 851’697 image triplets). As the instrument used for data collection was a Multi-Angle Snowflake Camera (MASC), the dataset is named MASCDB. Given the large amount of snowflake images and associated descriptors, MASCDB can be exploited also by the computer vision community for the training and benchmarking of image processing systems. -
Helms, C. N., Munchak, S. J., Tokay, A., and Pettersen, C.: A comparative evaluation of snowflake particle shape estimation techniques used by the Precipitation Imaging Package (PIP), Multi-Angle Snowflake Camera (MASC), and Two-Dimensional Video Disdrometer (2DVD), Atmos. Meas. Tech., 15, 6545–6561, doi:10.5194/amt-15-6545-2022, 2022. Link PDF
Abstract
Measurements of snowflake particle shape are important for studying snow microphysics. While a number of instruments exist that are capable of measuring particle shape, this study focuses on the measurement techniques of three digital video disdrometers: the Precipitation Imaging Package (PIP), the Multi-Angle Snowflake Camera (MASC), and the Two-Dimensional Video Disdrometer (2DVD). To gain a better understanding of the relative strengths and weaknesses of these instruments and to provide a foundation upon which comparisons can be made between studies using data from different instruments, we perform a comparative analysis of the shape measurement algorithms employed by each of the three instruments by applying the algorithms to snowflake images captured by PIP during the ICE-POP 2018 field campaign.Our analysis primarily focuses on the measurement of the aspect ratio of either the particle itself, in the case of PIP and MASC, or of the particle bounding box, in the case of PIP and 2DVD. Both PIP and MASC use shape-fitting algorithms to measure aspect ratio. While our analysis of the MASC aspect ratio suggests that the measurements are reliable, our findings indicate that both the ellipse and rectangle aspect ratios produced by PIP underperformed considerably due to the shortcomings of the PIP shape-fitting techniques. We also demonstrate that reliable measurements of aspect ratio can be retrieved from PIP by reprocessing the raw PIP images using either the MASC ellipse-fitting algorithm or a tensor-based ellipse-fitting algorithm. Because of differences in instrument design, 2DVD produces measurements of particle horizontal and vertical extent rather than length and width. Furthermore, the 2DVD measurements of particle horizontal extent can be contaminated by horizontal particle motion. Our findings indicate that, although the correction technique used to remove the horizontal motion contamination performs remarkably well with snowflakes despite being designed for use with raindrops, the 2DVD measurements of particle horizontal extent are less reliable than those measured by PIP.
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King, F., Duffy, G., Milani, L., Fletcher, C. G., Pettersen, C., and Ebell, K.: DeepPrecip: a deep neural network for precipitation retrievals, Atmos. Meas. Tech., 15, 6035–6050, doi:10.5194/amt-15-6035-2022, 2022. Link PDF
Abstract
Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval algorithm from a deep convolutional neural network (DeepPrecip) to estimate 20 min average surface precipitation accumulation using near-surface radar data inputs. DeepPrecip displays a high retrieval skill and can accurately model total precipitation accumulation, with a mean square error (MSE) 160 % lower, on average, than current methods. DeepPrecip also outperforms a less complex machine learning retrieval algorithm, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses suggest that a combination of both near-surface (below 1 km) and higher-altitude (1.5–2 km) radar measurements are the primary features contributing to retrieval accuracy. Further, DeepPrecip closely captures total precipitation accumulation magnitudes and variability across nine distinct locations without requiring any explicit descriptions of particle microphysics or geospatial covariates. This research reveals the important role for deep learning in extracting relevant information about precipitation from atmospheric radar retrievals. -
Ko, J.-S., Lim, K.-S. S., Kim, K., Lee, G., Thompson, G., and Berne, A.: Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the Weather Research and Forecasting (v4.1.3) model during the ICE-POP 2018 field campaign, Geosci. Model Dev., 15, 4529–4553, doi:10.5194/gmd-15-4529-2022, 2022. Link PDF
Abstract
This study evaluates the performance of four bulk-type microphysics schemes, Weather Research and Forecasting (WRF) double-moment 6-class (WDM6), WRF double-moment 7-class (WDM7), Thompson, and Morrison, focusing on hydrometeors and microphysics budgets in the WRF model version 4.1.3. Eight snowstorm cases, which can be sub-categorized as cold-low, warm-low, and air–sea interaction cases are selected, depending on the synoptic environment during the International Collaborative Experiment for Pyeongchang Olympics and Paralympics (ICE-POP 2018) field campaign. All simulations present a positive bias in the simulated surface precipitation for cold-low and warm-low cases. Furthermore, the simulations for the warm-low cases show a higher probability of detection score than simulations for the cold-low and air–sea interaction cases even though the simulations fail to capture the accurate transition layer for wind direction. WDM6 and WDM7 simulate abundant cloud ice for the cold-low and warm-low cases, and thus snow is mainly generated by aggregation. Meanwhile, Thompson and Morrison schemes simulate insignificant cloud ice amounts, especially over the lower atmosphere, where cloud water is simulated instead. Snow in the Thompson and Morrison schemes is mainly formed by the accretion between snow and cloud water and deposition. The melting process is analyzed as a key process to generate rain in all schemes. The discovered positive precipitation bias for the warm-low and cold-low cases can be mitigated by reducing the melting efficiency in all schemes. The contribution of melting to rain production is reduced for the air–sea interaction case with decreased solid-phase hydrometeors and increased cloud water in all simulations. -
Li, X., Roberts, J. B., Srikishen, J., Case, J. L., Petersen, W. A., Lee, G., and Hain, C. R.: Assimilation of GPM-retrieved ocean surface meteorology data for two snowstorm events during ICE-POP 2018, Geosci. Model Dev., 15, 5287–5308, doi:10.5194/gmd-15-5287-2022, 2022. Link PDF
Abstract
As a component of the National Aeronautics and Space Administration's (NASA's) Weather Focus Area and Global Precipitation Measurement (GPM) Ground Validation participation in the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field research and forecast demonstration programs, hourly ocean surface meteorology properties were retrieved from the GPM microwave observations for January–March 2018. In this study, the retrieved ocean surface meteorological products – 2 m temperature, 2 m specific humidity, and 10 m wind speed – were assimilated into a regional numerical weather prediction (NWP) framework. This explored the application of these observations for two heavy snowfall events during the ICE-POP 2018, on 27–28 February and 7–8 March 2018. The Weather Research and Forecasting (WRF) model and the community Gridpoint Statistical Interpolation (GSI) were used to conduct high-resolution simulations and data assimilation experiments. The results indicate that the data assimilation has a large influence on surface thermodynamic and wind fields in the model initial condition for both events. With cycled data assimilation, a significantly positive influence of the retrieved surface observation was found for the March case, with improved quantitative precipitation forecasts and reduced errors in temperature forecasts. A slightly smaller yet positive impact was also found in the forecast for the February case. -
Munchak, S. J., Schrom, R. S., Helms, C. N., and Tokay, A.: Snow microphysical retrieval from the NASA D3R radar during ICE-POP 2018, Atmos. Meas. Tech., 15, 1439–1464, doi:10.5194/amt-15-1439-2022, 2022. Link PDF
Abstract
A method is developed to use both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow. It is applied to the Ku- and Ka-band measurements of the NASA dual-polarization, dual-frequency Doppler radar (D3R) obtained during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign and incorporates the Atmospheric Radiative Transfer Simulator (ARTS) microwave single-scattering property database for oriented particles. The retrieval uses optimal estimation to solve for several parameters that describe the particle size distribution (PSD), relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously. Examination of Jacobian matrices and averaging kernels shows that the dual-wavelength ratio (DWR) measurements provide information regarding the characteristic particle size, and to a lesser extent, the rime fraction and shape parameter of the size distribution, whereas the polarimetric measurements provide information regarding the mass fraction of pristine particles and their characteristic size and orientation distribution. Thus, by combining the dual-frequency and polarimetric measurements, some ambiguities can be resolved that should allow a better determination of the PSD and bulk microphysical properties (e.g., snowfall rate) than can be retrieved from single-frequency polarimetric measurements or dual-frequency, single-polarization measurements.The D3R ICE-POP retrievals were validated using Precipitation Imaging Package (PIP) and Pluvio weighing gauge measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly, and its measurements can be used to derived the snowfall rate (volumetric and water equivalent), mean volume-weighted particle size, and effective density, as well as particle aspect ratio and orientation. Four retrieval experiments were performed to evaluate the utility of different measurement combinations: Ku-only, DWR-only, Ku-pol, and All-obs. In terms of correlation, the volumetric snowfall rate (r=0.95) and snow water equivalent rate (r=0.92) were best retrieved by the Ku-pol method, while the DWR-only method had the lowest magnitude bias for these parameters (−31 % and −8 %, respectively). The methods that incorporated DWR also had the best correlation to particle size (r=0.74 and r=0.71 for DWR-only and All-obs, respectively), although none of the methods retrieved density particularly well (r=0.43 for All-obs). The ability of the measurements to retrieve mean aspect ratio was also inconclusive, although the polarimetric methods (Ku-pol and All-obs) had reduced biases and mean absolute error (MAE) relative to the Ku-only and DWR-only methods. The significant biases in particle size and snowfall rate appeared to be related to biases in the measured DWR, emphasizing the need for accurate DWR measurements and frequent calibration in future D3R deployments.
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Park, J.-R., Kim, J.-H., Shin, Y., Kim, S.-H., Chun, H.-Y., Jang W., Tsai, C.-L., and Lee, G.: A Numerical Simulation of a Strong Windstorm Event in the Taebaek Mountain Region in Korea during the ICE-POP 2018. Atm. Res., 272, 106158, doi:10.1016/j.atmosres.2022.106158, 2022. Link PDF
Abstract
During the International Collaborative Experiments for the Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018), a strong windstorm occurred on the lee side of the Taebaek Mountains on 14 February 2018, when a low-pressure system passed through the northern part of the Korean Peninsula. At that time, a prevailing westerly wind with warm advection and an inversion layer at the top of the mountains provided favorable conditions for the downslope windstorm, resulting in structural damage at the Olympic Park. This led to the delay and cancellation of the ski jump and biathlon games. To investigate the generation mechanism responsible for the downslope windstorm during the ICE-POP 2018, we performed a numerical simulation using the Weather and Research Forecast model with the finest horizontal grid spacing of 333 m. The model effectively reproduced the multi-scale flows, such as the synoptic-scale low-pressure system, upstream sounding, and downslope winds, with slightly overestimated surface winds at some local sites on the lee side. Analysis of vertical cross-sections across the mountains showed a steep descent of potential temperature on the lee slope and a rapid recovery on the leeward side, showing the evidence of hydraulic jump with a Froude number of 0.9. During the windstorm event, mountain waves were generated with horizontal wavelengths that varied with time due to the change in background wind and stability along with movement of the low-pressure system. Using the dispersion relationship for internal gravity waves, the Scorer parameter for quasi-stationary mountain waves showed that the waves with horizontal wavelengths smaller than 10 km were trapped below the altitudes of 6–9 km. There were no signals of mountain wave breaking and wind reversal with height (wave-induced critical level), implying that the downslope windstorm event was generated by the mechanisms of hydraulic jump and partial reflection. Strengthened jet streams in the upstream and inversion layers at the top and gap winds in the valleys of the mountains also facilitated the downslope winds on the lee side. -
Tsai, C.-L., Kim, K., Liou, Y.-C., Kim, J.-H., Lee, Y. and Lee, G.: Orographic-induced strong wind associated with a low-pressure system under clear-air condition during ICE-POP 2018. J. Geophys. Res.-Atmos., 127, e2021JD036418, doi:10.1029/2021JD036418, 2022. Link
Abstract
A strong wind event under clear-air conditions during the 2018 Winter Olympic and Paralympic games in Pyeongchang, Korea, was examined using various datasets. High spatiotemporal resolution wind information was obtained by Doppler lidars, automatic weather stations, wind profiler, sounding observations, reanalysis datasets under the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This study aimed to understand the possible mechanisms of localized strong winds across a high mountainous area and on the leeside associated with the underlying large-scale pattern of a low-pressure system (LPS). The evolution of surface winds shows quite different patterns, exhibiting intensification of strong winds in the leeside and periodically persistent strong winds in upstream mountainous areas with the approaching LPS. The surface wind speed was intensified from ∼3 to ∼12 m s−1 (gusts were stronger than 20 m s−1 above the ground) at a surface station in the leeside. A budget analysis of the horizontal momentum equation suggested that the pressure gradient force (PGF) contributed from adiabatic warming and the passage of LPS was the main factor in the acceleration of the surface wind in the leeward side of the mountains. The detailed 3D winds revealed that the PGF also modulated the background winds at the mountainous station, which caused persistent strong and periodic winds (range of ∼7 to ∼12 m s−1) related to the channeling effect. The evidence showed that under the same synoptic condition of a LPS, different mechanisms are important for strong winds in determining the strength and persistence of orographic-induced strong winds under clear-air conditions. -
Yu, T., Chandrasekar, V., Xiao, H., Yang, L., Luo, L. and Li, X.: Dual-Frequency Radar Retrievals of Snowfall Using Random Forest. Remote Sens., 14(11), 2685. doi:10.3390/rs14112685, 2022. Link PDF
Abstract
The microphysical parameters of snowfall directly impact hydrological and atmospheric models. During the International Collaborative Experiment hosted at the Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018), dual-frequency radar retrievals of particle size distribution (PSD) parameters were produced and assessed over complex terrain. The NASA Dual-frequency Dual-polarized Doppler Radar (D3R) and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometer observations were used to develop retrievals. The conventional look-up table method (LUT) and random forest method (RF) were applied to the disdrometer data to develop retrievals for the volume-weighted mean diameter (Dm), the shape factor (mu), the normalized intercept parameter (Nw), the ice water content (IWC), and the snowfall rate (S). Evaluations were performed between the D3R radar and disdrometer observations using these two methods. The mean errors of the retrievals based on the RF method were small compared with those of the LUT method. The results indicate that the RF method is a promising way of retrieving microphysical parameters, because this method does not require any assumptions about the PSD of snowfall.
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Billault-Roux, A.-C., and Berne, A.: Integrated water vapor and liquid water path retrieval using a single-channel radiometer. Atmos. Meas. Tech., 14, 2749–2769, doi:10.5194/amt-14-2749-2021, 2021. Link PDF
Abstract
Microwave radiometers are widely used for the retrieval of liquid water path (LWP) and integrated water vapor (IWV) in the context of cloud and precipitation studies. This paper presents a new site-independent retrieval algorithm for LWP and IWV, relying on a single-frequency 89 GHz ground-based radiometer. A statistical approach is used based on a neural network, which is trained and tested on a synthetic dataset constructed from radiosonde profiles worldwide. In addition to 89 GHz brightness temperature, the input features include surface measurements of temperature, pressure, and humidity, as well as geographical information and, when available, estimates of IWV and LWP from reanalysis data. An analysis of the algorithm is presented to assess its accuracy, the impact of the various input features, its sensitivity to radiometer calibration, and its stability across geographical locations. While 89 GHz brightness temperature is crucial to LWP retrieval, it only moderately contributes to IWV estimation, which is more constrained by the additional input features. The algorithm is shown to be quite robust, although its accuracy is inevitably lower than that obtained with state-of-the-art multi-channel radiometers, with a relative error of 18 % for LWP (in cloudy cases with LWP >30 g m−2) and 6.5 % for IWV. The highest accuracy is obtained in midlatitude environments with a moderately moist climate, which are more represented in the training dataset. The new method is then implemented and evaluated on real data that were collected during a field deployment in Switzerland and during the ICE-POP 2018 campaign in South Korea. -
Gehring, J., Ferrone, A., Billault-Roux, A.-C., Besic, N., Ahn, K. D., Lee, G., and Berne, A.: Radar and ground-level measurements of precipitation collected by the École Polytechnique Fédérale de Lausanne during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games, Earth Syst. Sci. Data, 13, 417–433, doi:10.5194/essd-13-417-2021, 2021. Link PDF
Abstract
This article describes a 4-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.org/10.1594/PANGAEA.918315, Gehring et al., 2020a). Classifications of hydrometeor types derived from dual-polarisation measurements and snowflake photographs are presented. The dataset covers the period from 15 November 2017 to 18 March 2018 and features nine precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. This represents 85 % of the climatological accumulation over this period. To illustrate the available data, measurements corresponding to the four precipitation events with the largest accumulation are presented. The synoptic situations of these events were contrasted and influenced the precipitation type and accumulation. The hydrometeor classifications reveal that aggregate snowflakes were dominant and that some events featured significant riming. The combination of dual-polarisation variables and high-resolution Doppler spectra with ground-level snowflake images makes this dataset particularly suited to study snowfall microphysics in a region where such measurements were not available before. -
Jang, S., Lim, K.-S. S., Ko, J., Kim, K., Lee, G., Cho, S.-J., Ahn, K.-D., Lee, Y.-H.: Revision of WDM7 Microphysics Scheme and Evaluation for Precipitating Convection over the Korean Peninsula. Remote Sens., 13, 3860, doi:10.3390/rs13193860, 2021. Link PDF
Abstract
The Weather Research and Forecasting (WRF) Double-Moment 7-Class (WDM7) cloud microphysics scheme was developed to parameterize cloud and precipitation processes explicitly for mesoscale phenomena in the Korean Integrated Model system. However, the WDM7 scheme has not been evaluated for any precipitating convection system over the Korean peninsula. This study modified WDM7 and evaluated simulated convection during summer and winter. The suggested modifications included the integration of the new fall velocity–diameter relationship of raindrops and mass-weighted terminal velocity of solid-phase precipitable hydrometeors (the latter is for representing mixed-phase particles). The mass-weighted terminal velocity for snow and graupel has been suggested by Dudhia et al. (2008) to allow for a more realistic representation of partially rimed particles. The WDM7 scheme having an additional hail category does not apply this terminal velocity only for hail. Additionally, the impact of enhanced collision-coalescence (C-C) efficiency was investigated. An experiment with enhanced C-C efficiency overall improved the precipitation skill scores, such as probability of detection, equitable threat score, and spatial pattern correlation, compared with those of the control experiment for the summer and winter cases. With application of the new mass-weighted terminal velocity of solid-phase hydrometeors, the hail mixing ratio at the surface was considerably reduced, and rain shafts slowed down low-level winds for the winter convective system. Consequently, the simulated hydrometeors were consistent with observations retrieved via remote sensing. The fall velocity–diameter relationship of raindrops further reduced the cloud ice amount. The proposed modifications in our study improved the simulated precipitation and hydrometeor profiles, especially for the selected winter convection case. -
Kim, K., Bang, W., Chang, E.-C., Tapiador, F. J., Tsai, C.-L., Jung, E., and Lee, G.: Impact of wind pattern and complex topography on snow microphysics during International Collaborative Experiment for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018), Atmos. Chem. Phys., 21, 11955–11978, doi:10.5194/acp-21-11955-2021, 2021. Link PDF
Abstract
Snowfall in the northeastern part of South Korea is the result of complex snowfall mechanisms due to a highly contrasting terrain combined with nearby warm waters and three synoptic pressure patterns. All these factors together create unique combinations, whose disentangling can provide new insights into the microphysics of snow on the planet. This study focuses on the impact of wind flow and topography on the microphysics drawing of 20 snowfall events during the ICE-POP 2018 (International Collaborative Experiment for PyeongChang 2018 Olympic and Paralympic winter games) field campaign in the Gangwon region. The vertical structure of precipitation and size distribution characteristics are investigated with collocated MRR (micro rain radar) and PARSIVEL (particle size velocity) disdrometers installed across the mountain range. The results indicate that wind shear and embedded turbulence were the cause of the riming process dominating the mountainous region. As the strength of these processes weakens from the mountainous region to the coastal region, riming became less significant and gave way to aggregation. This study specifically analyzes the microphysical characteristics under three major synoptic patterns: air–sea interaction, cold low, and warm low. Air–sea interaction pattern is characterized by more frequent snowfall and vertically deeper precipitation systems on the windward side, resulting in significant aggregation in the coastal region, with riming featuring as a primary growth mechanism in both mountainous and coastal regions. The cold-low pattern is characterized by a higher snowfall rate and vertically deep systems in the mountainous region, with the precipitation system becoming shallower in the coastal region and strong turbulence being found in the layer below 2 km in the mountainous upstream region (linked with dominant aggregation). The warm-low pattern features the deepest system: precipitation here is enhanced by the seeder–feeder mechanism with two different precipitation systems divided by the transition zone (easterly below and westerly above). Overall, it is found that strong shear and turbulence in the transition zone is a likely reason for the dominant riming process in the mountainous region, with aggregation being important in both mountainous and coastal regions. -
Notaros, B.: Meteorological Electromagnetics: Optical and Radar Measurements, Modeling, and Characterization of Snowflakes and Snow. IEEE Antennas Propag. Mag., 63, 14–27, doi:10.1109/MAP.2021.3054298, 2021. Link PDF
Abstract
We introduce the concepts, methodologies, and applications of meteorological electromagnetics with a focus on snow, which currently is the least understood component of the global water cycle. As "no two snowflakes are alike," the intricacies of snowflakes and snowfall are both truly fascinating and extremely challenging to measure, analyze, and predict. We describe a unique approach to the characterization of winter precipitation through the synergistic use of advanced optical instrumentation for in situ microphysical and geometrical measurements of ice and snow particles; image processing techniques to obtain the fall speed, size distribution, 3D shape (mesh), density, and effective dielectric constant of snowflakes; method of moments (MoM) scattering computations of precipitation particles; and state-of-the-art dualpolarization radars for the measurement of polarimetric scattering observables. We discuss the operations, observations, and analyses using this approach during a snow field campaign that took place in Colorado, United States, from 2014 to 2017, and we also introduce an international collaborative field program in association with the 2018 Winter Olympics in South Korea. One goal of this article is to promote meteorological electromagnetics as an interdisciplinary field where nature, science, and technology meet in some of the most fascinating and rewarding ways and where many key areas of interest and endeavors of the antennas and propagation community play an indispensable role. -
Planat, N., Gehring, J., Vignon, É., and Berne, A.: Identification of snowfall microphysical processes from Eulerian vertical gradients of polarimetric radar variables, Atmos. Meas. Tech., 14, 4543–4564, doi:10.5194/amt-14-4543-2021, 2021. Link PDF
Abstract
Polarimetric radar systems are commonly used to study the microphysics of precipitation. While they offer continuous measurements with a large spatial coverage, retrieving information about the microphysical processes that govern the evolution of snowfall from the polarimetric signal is challenging. The present study develops a new method, called process identification based on vertical gradient signs (PIVSs), to spatially identify the occurrence of the main microphysical processes (aggregation and riming, crystal growth by vapor deposition and sublimation) in snowfall from dual-polarization Doppler radar scans. We first derive an analytical framework to assess in which meteorological conditions the local vertical gradients of radar variables reliably inform about microphysical processes. In such conditions, we then identify regions dominated by (i) vapor deposition, (ii) aggregation and riming and (iii) snowflake sublimation and possibly snowflake breakup, based on the sign of the local vertical gradients of the reflectivity ZH and the differential reflectivity ZDR. The method is then applied to data from two frontal snowfall events, namely one in coastal Adélie Land, Antarctica, and one in the Taebaek Mountains in South Korea. The validity of the method is assessed by comparing its outcome with snowflake observations, using a multi-angle snowflake camera, and with the output of a hydrometeor classification, based on polarimetric radar signal. The application of the method further makes it possible to better characterize and understand how snowfall forms, grows and decays in two different geographical and meteorological contexts. In particular, we are able to automatically derive and discuss the altitude and thickness of the layers where each process prevails for both case studies. We infer some microphysical characteristics in terms of radar variables from statistical analysis of the method output (e.g., ZH and ZDR distribution for each process). We, finally, highlight the potential for extensive application to cold precipitation events in different meteorological contexts. -
Tapiador, F. J., Villalba-Pradas, A., Navarro, A., García-Ortega, E., Lim, K.-S. S., Kim, K., Ahn, K. D., Lee, G.: Future Directions in Precipitation Science. Remote Sens., 13, 1074, doi:10.3390/rs13061074, 2021. Link PDF
Abstract
Precipitation science is a growing research field. It is concerned with the study of the water cycle from a broad perspective, from tropical to polar research and from solid precipitation to humidity and microphysics. It includes both modeling and observations. Drawing on the results of several meetings within the International Collaborative Experiments for the PyeongChang 2018 Olympics and Paralympic Winter Games (ICE-POP 2018), and on two Special Issues hosted by Remote Sensing starting with “Winter weather research in complex terrain during ICE-POP 2018”, this paper completes the “Precipitation and Water Cycle” Special Issue by providing a perspective on the future research directions in the field. -
Yu, T., Chandrasekar, V., Xiao, H. and Joshil, S. S.: Snowfall Estimation Using Dual-wavelength Radar during the PyeongChang 2018 Olympics and Paralympic winter games, J. Meteorol. Soc. Japan, Advanced online publication, doi:10.2151/jmsj.2021, 2021. Link PDF
Abstract
Accurate estimation of snowfall rate during snowstorms is crucial. This estimate directly impacts the hydrological and atmospheric models. The density of snow plays a very important role in estimating the snowfall rate. In this paper, the density of snow is investigated during a huge snowstorm event during the International Collaborative Experiment held during the PyeongChang 2018 Olympics and Paralympic winter games (ICE-POP 2018). The density is calculated using the terminal velocities and diameters of the snow particles measured by a disdrometer. In this study, we not only use radar reflectivity factor (Z) for snowfall rate (S) estimation, but also use dual-frequency ratio (DFR). We derive S-Z and S-Z-DFR relations for snowfall estimation during this snowstorm event after considering the density of snow. The comparisons are performed between National Aeronautics and Space Administration (NASA) Dual-frequency Dual-polarization Doppler Radar (D3R) and precipitation gauges using these two power-law relations. The results show that the two relations for snowfall rate estimation agree well with gauges, but the S-Z-DFR method performs the best, which has a lower normalized standard error. The error in the snowfall rate estimates decreases as the time scale becomes large. This shows that the S-Z-DFR algorithm is a promising way for snowfall quantitative precipitation estimation (QPE) and can be used as a ground validation tool for Global Precipitation Measurement (GPM) snowfall production evaluations.
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Gehring, J., Oertel, A., Vignon, É., Jullien, N., Besic, N. and Berne, A.: Microphysics and dynamics of snowfall associated with a warm conveyor belt over Korea, Atmos. Chem. Phys., 20(12), 7373–7392, doi:10.5194/acp-20-7373-2020, 2020. Link PDF
Abstract
On 28 February 2018, 57 mm of precipitation associated with a warm conveyor belt (WCB) fell within 21 h over South Korea. To investigate how the large-scale circulation influenced the microphysics of this intense precipitation event, we used radar measurements, snowflake photographs and radiosounding data from the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018). The WCB was identified with trajectories computed with analysis wind fields from the Integrated Forecast System global atmospheric model. The WCB was collocated with a zone of enhanced wind speed of up to 45 m s−1 at 6500 m a.s.l., as measured by a radiosonde and a Doppler radar. Supercooled liquid water (SLW) with concentrations exceeding 0.2 g kg−1 was produced during the rapid ascent within the WCB. During the most intense precipitation period, vertical profiles of polarimetric radar variables show a peak and subsequent decrease in differential reflectivity as aggregation starts. Below the peak in differential reflectivity, the specific differential phase shift continues to increase, indicating early riming of oblate crystals and secondary ice generation. We hypothesise that the SLW produced in the WCB led to intense riming. Moreover, embedded updraughts in the WCB and turbulence at its lower boundary enhanced aggregation by increasing the probability of collisions between particles. This suggests that both aggregation and riming occurred prominently in this WCB. This case study shows how the large-scale atmospheric flow of a WCB provides ideal conditions for rapid precipitation growth involving SLW production, riming and aggregation. Future microphysical studies should also investigate the synoptic conditions to understand how observed processes in clouds are related to large-scale circulation. -
Jeoung, H., Liu, G., Kim, K., Lee, G., and Seo, E.-K.: Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals, Atmos. Chem. Phys., 20(23), 14491–14507, doi:10.5194/acp-20-14491-2020, 2020. Link PDF
Abstract
Ground-based radar and radiometer data observed during the 2017–2018 winter season over the Pyeongchang area on the east coast of the Korean Peninsula were used to simultaneously estimate both the cloud liquid water path and snowfall rate for three types of snow clouds: near-surface, shallow, and deep. Surveying all the observed data, it is found that near-surface clouds are the most frequently observed cloud type with an area fraction of over 60 %, while deep clouds contribute the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with the vast majority hardly reaching 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, the liquid water paths in the three types of clouds all have the substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and the liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager (GPM/GMI) channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as an a priori database. Under an idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution, and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.32, 0.41, and 0.62, respectively, for near-surface, shallow, and deep snow clouds over land surfaces; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increasing from 0.32 to 0.52, while a smaller improvement is found for shallow and deep clouds. This study provides a general picture of the microphysical characteristics of the different types of snow clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations. -
Kim, T. M., Lee, S. J., Ahn, M. H., and Chung, S. R.: Evaluation of atmospheric profile retrieval algorithm for GK2A satellite with dropsonde observations, Asia-Pac. J. Atmos. Sci., 56, 225–233, doi:10.1007/s13143-019-00154-5, 2020. Link PDF
Abstract
The Korea’s Geostationary Multi-Purpose Satellite-2A (GK2A) was launched in December 2018 with its main payload, Advanced Meteorological Imager (AMI). Using the 9 infrared channels of the AMI, an algorithm has been developed to retrieve vertical profiles of temperature and humidity for the clear-sky. The algorithm, named as AMI Atmospheric Profile (AAP), is based on an optimal estimation method with its a priori information from model forecasts. From the retrieved profiles, total precipitable water and atmospheric instability indices are estimated, while total column ozone is derived as a by-product. Comparisons of the AAP products and radiosonde observations over land surface stations show that the AAP humidity profiles improve the a priori information by about 4% of root mean square error (RMSE) between 100 and 1000 hPa. However, as most of radiosonde data used for the validation are also used for the preparation of the a priori data, comparisons with radiosonde data would not reveal full characteristics of the AAP algorithm. Therefore, current study evaluates the AAP using dropsonde data obtained over the ocean. Since the dropsonde data are not used for the preparation of the a priori data, they could be a truly independent reference data set. The validation results with the dropsonde data confirm that the AAP performance over the ocean is almost the same as that on land for the temperature profiles. In the case of the humidity, however, a priori improvements by AAP algorithm is much larger over the ocean than on land (the RMSE is improved by 11% between the surface and 400 hPa). The results also show that the performance of the retrieval algorithm under clear-sky conditions is similar to that at the cloud edges over the sea, suggesting the potential benefits of using AAP temperature and humidity profiles for real-time analysis of the atmospheric conditions over the ocean. -
La, I., Yum, S. S., Gultepe, I., Yeom, J. M., Song, J. I. and Cha, J. W.: Influence of quasi-periodic oscillation of atmospheric variables on radiation fog over a mountainous region of Korea, Atmosphere, 11(3), doi:10.3390/atmos11030230, 2020. Link PDF
Abstract
To enhance our understanding of fog processes over complex terrain, various fog events that occurred during the International Collaborative Experiments for Pyeongchang 2018 Winter Olympics and Paralympics (ICE-POP) campaign were selected. Investigation of thermodynamic, dynamic, and microphysical conditions within fog layers affected by quasi-periodic oscillation of atmospheric variables was conducted using observations from a Fog Monitor-120 (FM-120) and other in-situ meteorological instruments. A total of nine radiation fog cases that occurred in the autumn and winter seasons during the campaign over the mountainous region of Pyeongchang, Korea were selected. The wavelet analysis was used to study quasi-period oscillations of dynamic, microphysical, and thermodynamic variables. By decomposing the time series into the time-frequency space, we can determine both dominant periods and how these dominant periods change in time. Quasi-period oscillations of liquid water content (LWC), pressure, temperature, and horizontal/vertical velocity, which have periods of 15–40 min, were observed during the fog formation stages. We hypothesize that these quasi-periodic oscillations were induced by Kelvin–Helmholtz instability. The results suggest that Kelvin–Helmholtz instability events near the surface can be explained by an increase in the vertical shear of horizontal wind and by a simultaneous increase in wind speed when fog forms. In the mature stages, fluctuations of the variables did not appear near the surface anymore. -
Lim, K.-S. S., Chang, E.-C., Sun, R., Kim, K., Tapiador, F. J. and Lee, G.: Evaluation of Simulated Winter Precipitation Using WRF-ARW during the ICE-POP 2018 Field Campaign, Weather Forecast., 35(5), 2199–2213, doi:10.1175/WAF-D-19-0236.1, 2020. Link
Abstract
This study evaluates the performance of several cloud microphysics parameterizations in simulating surface precipitation for two snowstorm cases during the International Collaborative Experiment held at the PyeongChang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign. We compared four different schemes in the Weather Research and Forecasting (WRF) Model, namely the double-moment 6-class (WDM6), the WRF single-moment 6-class (WSM6), and Thompson and Morrison parameterizations. Both WSM6 and WDM6 overestimated the precipitation amount for the shallow precipitation system because of the substantial amount of cloud ice, mostly generated by the deposition process. The simulated precipitation amount and distribution for the deep precipitation system showed no noticeable differences in the different cloud microphysics parameterizations. However, the simulated hydrometeor type at the surface using WSM6 and WDM6 showed good agreement with observations for all cases. The accuracy of the mean mass-weighted terminal velocity of cloud ice VI¯ applied in WSM6 and WDM6 is ±20%. The number concentration of cloud ice and the ice microphysics processes are newly retrieved with 1.2 times increased VI¯. For the shallow snowstorm, the precipitation amount was reduced by approximately 8% because of the inefficient deposition and its effects on the subsequent ice microphysical processes, such as the accretion of cloud ice by snow and the conversion from cloud ice to snow. -
Moreno, R., Arias, E., Cazorla, D., Pardo, J. J., Navarro, A., Rojo, T. and Tapiador, F. J.: Analysis of a New MPI Process Distribution for the Weather Research and Forecasting (WRF) Model, Sci. Program., 2020, doi:10.1155/2020/8148373, 2020. Link PDF
Abstract
The standard method used in the Weather Research and Forecasting (WRF) model for distributing MPI processes across the processors is not always optimal. This circumstance affects performance, i.e., execution times, but also energy consumption, especially if the application is to be extended to exascale. The authors found that the reason why the standard method for process distribution is not always optimal was an imbalance between the orthogonality of the communication and the proper cache usage, and this affects energy consumption. We present an improved MPI process distribution algorithm that increases the performance. Furthermore, scalability analyses for the new algorithm are presented and the energy use of the system is evaluated. A solution for balancing energy use with performance is also proposed for cases where the former is a concern. -
Moreno, R., Arias, E., Cazorla, D., Pardo, J. J. and Tapiador, F. J.: Seeking the best Weather Research and Forecasting model performance: an empirical score approach, J. Supercomput., 76(12), 9629–9653, doi:10.1007/s11227-020-03219-9, 2020. Link PDF
Abstract
Weather forecasting, especially snowfall prediction, was critical in the 2018 Winter Olympics, where the accuracy of the predictions was of key importance for the planning of the different Olympic events. It was a significant challenge for the authors to meet the requirements in time and forecast resolution, while doing their best to be as competitive as possible. All the forecasts were obtained using the Weather Research and Forecasting (WRF) model, executed on the GALGO supercomputer. In order to obtain the best performance and meet the required execution times, different combinations of compilers, Message Passing Interface (MPI) libraries and computing platforms were tested to seek the best combinations. This work proposes an empirical score of special interest to supercomputer maintainers, developers and scientists, which can be useful to obtain the best WRF configuration for their systems. Additionally, we found substantial performance differences when using different combinations of compilers, MPI libraries and hybrid shared memory paradigms, although these differences varied depending on the underlying platform. As conclusion, after all the tests we performed, we chose the combination with Intel compilers, Intel MPI library and OpenMP for the production system tasked to perform the weather forecasts for the Winter Olympic Games. -
Yu, T., Chandrasekar, V., Xiao, H. and Joshil, S. S.: Characteristics of Snow Particle Size Distribution in the PyeongChang Region of South Korea, Atmosphere, 11(10), 1093, doi:10.3390/atmos11101093, 2020. Link PDF
Abstract
Snow particle size distribution (PSD) information is important in understanding the microphysics and quantitative precipitation estimation over complex terrain. Measurement and interpretation of the snow PSDs is a topic of active research. This study investigates snow PSDs during 3 year of observations from Parsivel2 disdrometers and precipitation imaging packages (PIP) at five different sites in the PyeongChang region of South Korea. Variabilities in the values of the density of snow (ρ), snowfall rate (S), and ice water content (IWC) are studied. To further understand the characteristics of snow PSD at different density and snowfall rate, the snow particle size distribution measurements are divided into six classes based on the density values of snowfall and five classes based on snowfall rates. The mean shape factors (Dm, log10Nw, and μ) of normalized gamma distribution are also derived based on different density and snowfall rate classes. The Dm decreases and log10Nw and μ increase as the density increases. The Dm and log10Nw increase and μ decreases with the increase of snowfall rate. The power-law relationship between ρ and Dm is obtained and the relationship between S and IWC is also derived.
- Lee, Y. H., Lee, G., Joo, S. and Ahn, K. D.: Observational study of surface wind along a sloping surface over mountainous terrain during winter, Adv. Atmos. Sci., 35(3), 276–284, doi:10.1007/s00376-017-7075-5, 2018. Link PDF
Abstract
The 2018 Winter Olympic and Paralympic Games will be held in Pyeongchang, Korea, during February and March. We examined the near surface winds and wind gusts along the sloping surface at two outdoor venues in Pyeongchang during February and March using surface wind data. The outdoor venues are located in a complex, mountainous terrain, and hence the near-surface winds form intricate patterns due to the interplay between large-scale and locally forced winds. During February and March, the dominant wind at the ridge level is westerly; however, a significant wind direction change is observed along the sloping surface at the venues. The winds on the sloping surface are also influenced by thermal forcing, showing increased upslope flow during daytime. When neutral air flows over the hill, the windward and leeward flows show a significantly different behavior. A higher correlation of the wind speed between upper- and lower-level stations is shown in the windward region compared with the leeward region. The strong synoptic wind, small width of the ridge, and steep leeward ridge slope angle provide favorable conditions for flow separation at the leeward foot of the ridge. The gust factor increases with decreasing surface elevation and is larger during daytime than nighttime. A significantly large gust factor is also observed in the leeward region.