Articles | Volume 15, issue 13
https://doi.org/10.5194/gmd-15-5287-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-5287-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 Jul 2022
Development and technical paper |
| 12 Jul 2022
| 12 Jul 2022
Assimilation of GPM-retrieved ocean surface meteorology data for two snowstorm events during ICE-POP 2018
Xuanli Li
CORRESPONDING AUTHOR
Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama, USA
Jason B. Roberts
NASA Marshall Space Flight Center, Huntsville, Alabama, USA
Jayanthi Srikishen
Science and Technology Institute, Universities Space Research Association, Huntsville, Alabama, USA
Jonathan L. Case
ENSCO, Inc./NASA SPoRT Center, Huntsville, Alabama, USA
Walter A. Petersen
NASA Marshall Space Flight Center, Huntsville, Alabama, USA
Gyuwon Lee
Department of Astronomy and Atmospheric Sciences, Center for Atmospheric REmote sensing (CARE), Kyungpook National University, Daegu, Republic of Korea
Christopher R. Hain
NASA Marshall Space Flight Center, Huntsville, Alabama, USA
Related authors
No articles found.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary
Short summary
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Chia-Lun Tsai, Kwonil Kim, Yu-Chieng Liou, and GyuWon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2025-1908, https://doi.org/10.5194/egusphere-2025-1908, 2025
Short summary
Short summary
The WISSDOM is a practical scheme to derive 3D winds by using 11 radars in this study. The observations of shot-wavelength radars (i.e., C- and X-band) can be attributed to additional low-level precipitation and wind information in WISSDOM, which allowed for the capture of stronger updrafts in the convection areas of the squall line. Overall, these results highlight the advantages of using radars with multiple wavelengths in WISSDOM, especially C- and X-band radars.
Wei-Yu Chang, Yung-Chuan Yang, Chen-Yu Hung, Kwonil Kim, Gyuwon Lee, and Ali Tokay
Atmos. Chem. Phys., 24, 11955–11979, https://doi.org/10.5194/acp-24-11955-2024, https://doi.org/10.5194/acp-24-11955-2024, 2024
Short summary
Short summary
Snow density is derived by collocated Micro-Rain Radar (MRR) and Parsivel (ICE-POP 2017/2018). We apply the particle size distribution from Parsivel to a T-matrix backscattering simulation and compare with ZHH from MRR. Bulk density and bulk water fractions are derived from comparing simulated and calculated ZHH. Retrieved bulk density is validated by comparing snowfall rate measurements from Pluvio and the Precipitation Imaging Package. Snowfall rate consistency confirms the algorithm.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
Short summary
Short summary
We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
R. Bradley Pierce, Monica Harkey, Allen Lenzen, Lee M. Cronce, Jason A. Otkin, Jonathan L. Case, David S. Henderson, Zac Adelman, Tsengel Nergui, and Christopher R. Hain
Atmos. Chem. Phys., 23, 9613–9635, https://doi.org/10.5194/acp-23-9613-2023, https://doi.org/10.5194/acp-23-9613-2023, 2023
Short summary
Short summary
We evaluate two high-resolution model simulations with different meteorological inputs but identical chemistry and anthropogenic emissions, with the goal of identifying a model configuration best suited for characterizing air quality in locations where lake breezes commonly affect local air quality along the Lake Michigan shoreline. This analysis complements other studies in evaluating the impact of meteorological inputs and parameterizations on air quality in a complex environment.
Jason A. Otkin, Lee M. Cronce, Jonathan L. Case, R. Bradley Pierce, Monica Harkey, Allen Lenzen, David S. Henderson, Zac Adelman, Tsengel Nergui, and Christopher R. Hain
Atmos. Chem. Phys., 23, 7935–7954, https://doi.org/10.5194/acp-23-7935-2023, https://doi.org/10.5194/acp-23-7935-2023, 2023
Short summary
Short summary
We performed model simulations to assess the impact of different parameterization schemes, surface initialization datasets, and analysis nudging on lower-tropospheric conditions near Lake Michigan. Simulations were run with high-resolution, real-time datasets depicting lake surface temperatures, green vegetation fraction, and soil moisture. The most accurate results were obtained when using high-resolution sea surface temperature and soil datasets to constrain the model simulations.
Chia-Lun Tsai, Kwonil Kim, Yu-Chieng Liou, and GyuWon Lee
Atmos. Meas. Tech., 16, 845–869, https://doi.org/10.5194/amt-16-845-2023, https://doi.org/10.5194/amt-16-845-2023, 2023
Short summary
Short summary
Since the winds in clear-air conditions usually play an important role in the initiation of various weather systems and phenomena, the modified Wind Synthesis System using Doppler Measurements (WISSDOM) synthesis scheme was developed to derive high-quality and high-spatial-resolution 3D winds under clear-air conditions. The performance and accuracy of derived 3D winds from this modified scheme were evaluated with an extreme strong wind event over complex terrain in Pyeongchang, South Korea.
Jeong-Su Ko, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, Gregory Thompson, and Alexis Berne
Geosci. Model Dev., 15, 4529–4553, https://doi.org/10.5194/gmd-15-4529-2022, https://doi.org/10.5194/gmd-15-4529-2022, 2022
Short summary
Short summary
This study evaluates the performance of the four microphysics parameterizations, the WDM6, WDM7, Thompson, and Morrison schemes, in simulating snowfall events during the ICE-POP 2018 field campaign. Eight snowfall events are selected and classified into three categories (cold-low, warm-low, and air–sea interaction cases). The evaluation focuses on the simulated hydrometeors, microphysics budgets, wind fields, and precipitation using the measurement data.
Ki-Hong Min, Kao-Shen Chung, Ji-Won Lee, Cheng-Rong You, and Gyuwon Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-18, https://doi.org/10.5194/gmd-2022-18, 2022
Revised manuscript not accepted
Short summary
Short summary
LETKF underestimated the water vapor mixing ratio and temperature compared to 3DVAR due to a lack of a water vapor mixing ratio and temperature observation operator. Snowfall in GWD was less simulated in LETKF. The results signify that water vapor assimilation is important in radar DA and significantly impacts precipitation forecasts, regardless of the DA method used. Therefore, it is necessary to apply observation operators for water vapor mixing ratio and temperature in radar DA.
Paul Joe, Gyuwon Lee, and Kwonil Kim
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-620, https://doi.org/10.5194/acp-2021-620, 2021
Preprint withdrawn
Short summary
Short summary
Strong gusty wind events were responsible for poor performance of competitors and schedule changes during the PyeongChang 2018 Olympic and Paralympic Winter Games. Three events were investigated and documented to articulate the challenges confronting forecasters which is beyond what they normally do. Quantitative evidence of the challenge and recommendations for future Olympics are provided.
Kwonil Kim, Wonbae Bang, Eun-Chul Chang, Francisco J. Tapiador, Chia-Lun Tsai, Eunsil Jung, and Gyuwon Lee
Atmos. Chem. Phys., 21, 11955–11978, https://doi.org/10.5194/acp-21-11955-2021, https://doi.org/10.5194/acp-21-11955-2021, 2021
Short summary
Short summary
This study analyzes the microphysical characteristics of snow in complex terrain and the nearby ocean according to topography and wind pattern during the ICE-POP 2018 campaign. The observations from collocated vertically pointing radars and disdrometers indicate that the riming in the mountainous region is likely caused by a strong shear and turbulence. The different behaviors of aggregation and riming were found by three different synoptic patterns (air–sea interaction, cold low, and warm low).
Anam M. Khan, Paul C. Stoy, James T. Douglas, Martha Anderson, George Diak, Jason A. Otkin, Christopher Hain, Elizabeth M. Rehbein, and Joel McCorkel
Biogeosciences, 18, 4117–4141, https://doi.org/10.5194/bg-18-4117-2021, https://doi.org/10.5194/bg-18-4117-2021, 2021
Short summary
Short summary
Remote sensing has played an important role in the study of land surface processes. Geostationary satellites, such as the GOES-R series, can observe the Earth every 5–15 min, providing us with more observations than widely used polar-orbiting satellites. Here, we outline current efforts utilizing geostationary observations in environmental science and look towards the future of GOES observations in the carbon cycle, ecosystem disturbance, and other areas of application in environmental science.
Chia-Lun Tsai, Kwonil Kim, Yu-Chieng Liou, Jung-Hoon Kim, YongHee Lee, and GyuWon Lee
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-100, https://doi.org/10.5194/acp-2021-100, 2021
Preprint withdrawn
Short summary
Short summary
This study examines a strong downslope wind event during ICE-POP 2018 using Doppler lidars, and observations. 3D winds can be well retrieved by
WISSDOM. This is first time to document the mechanisms of strong wind in observational aspect under fine weather. The PGF causing by adiabatic warming and channeling effect are key factors to dominate the strong wind. The values of this study are improving our understanding of the strong wind and increase the predictability of the weather forecast.
Josué Gehring, Alfonso Ferrone, Anne-Claire Billault-Roux, Nikola Besic, Kwang Deuk Ahn, GyuWon Lee, and Alexis Berne
Earth Syst. Sci. Data, 13, 417–433, https://doi.org/10.5194/essd-13-417-2021, https://doi.org/10.5194/essd-13-417-2021, 2021
Short summary
Short summary
This article describes a dataset of precipitation and cloud measurements collected from November 2017 to March 2018 in Pyeongchang, South Korea. The dataset includes weather radar data and images of snowflakes. It allows for studying the snowfall intensity; wind conditions; and shape, size and fall speed of snowflakes. Classifications of the types of snowflakes show that aggregates of ice crystals were dominant. This dataset represents a unique opportunity to study snowfall in this region.
Marloes Gutenstein, Karsten Fennig, Marc Schröder, Tim Trent, Stephan Bakan, J. Brent Roberts, and Franklin R. Robertson
Hydrol. Earth Syst. Sci., 25, 121–146, https://doi.org/10.5194/hess-25-121-2021, https://doi.org/10.5194/hess-25-121-2021, 2021
Short summary
Short summary
The net exchange of water between the surface and atmosphere is mainly determined by the freshwater flux: the difference between evaporation (E) and precipitation (P), or E−P. Although there is consensus among modelers that with a warming climate E−P will increase, evidence from satellite data is still not conclusive, mainly due to sensor calibration issues. We here investigate the degree of correspondence among six recent
satellite-based climate data records and ERA5 reanalysis E−P data.
Hwayoung Jeoung, Guosheng Liu, Kwonil Kim, Gyuwon Lee, and Eun-Kyoung Seo
Atmos. Chem. Phys., 20, 14491–14507, https://doi.org/10.5194/acp-20-14491-2020, https://doi.org/10.5194/acp-20-14491-2020, 2020
Short summary
Short summary
Radar and radiometer observations were used to study cloud liquid and snowfall in three types of snow clouds. While near-surface and shallow clouds have an area fraction of 90 %, deep clouds contribute half of the total snowfall volume. Deeper clouds have heavier snowfall, although cloud liquid is equally abundant in all three cloud types. The skills of a GMI Bayesian algorithm are examined. Snowfall in deep clouds may be reasonably retrieved, but it is challenging for near-surface clouds.
Cited articles
Alcott, T. and Steenburgh, W.: Snow-to-Liquid Ratio Variability and Prediction at a High-Elevation Site in Utah's Wasatch Mountains, Weather Forecast., 25, 323–337,
https://doi.org/10.1175/2009WAF2222311.1, 2010.
Berg, W., Kroodsma, R., Kummerow, C., McKague, D., Berg, W., Kroodsma, R.,
Kummerow, C. D., and McKague, D. S.: Fundamental Climate Data Records of
Microwave Brightness Temperatures, Remote Sens., 10, 1306,
https://doi.org/10.3390/rs10081306, 2018.
Call, D. A.: Changes in ice storm impacts over time: 1886–2000, Wea.
Climate. Soc., 2, 23–35, https://doi.org/10.1175/2009WCAS1013.1, 2010.
Chang, C. P., Wang, Z., and Hendon, H.: The Asian winter monsoon, in: The
Asian Monsoon, Springer Praxis Books, Springer, Berlin, Heidelberg,
https://doi.org/10.1007/3-540-37722-0_3, 2006.
Changnon, S. A.: Characteristics of ice storms in the United States, J.
Appl. Meteor., 42, 630–639,
https://doi.org/10.1175/1520-0450(2003)042<0630:COISIT>2.0.CO;2, 2003.
Changnon, S. A.: Catastrophic winter storms: An escalating problem, Climatic
Change, 84, 131–139, https://doi.org/10.1007/s10584-007-9289-5, 2007.
Chen, F. and Dudhia, J.: Coupling an advanced land-surface/ hydrology model
with the Penn State/ NCAR MM5 modeling system. Part I: Model description and
implementation, Mon. Weather Rev., 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Chou, M.-D. and Suarez, M. J.: A solar radiation parameterization for
atmospheric studies, NASA Tech. Memo. 104606, NASA, Greenbelt, MD., 40 pp.,
https://ntrs.nasa.gov/citations/19990060930 (last access: 7 July 2022), 1999.
Cucurull, L., Vandenberghe, F., Barker, D., Vilaclara, E., and Rius, A.:
Three-Dimensional Variational Data Assimilation of Ground-Based GPS ZTD and
Meteorological Observations during the 14 December 2001 Storm Event over the
Western Mediterranean Sea, Mon. Weather Rev., 132, 749–763,
https://doi.org/10.1175/1520-0493(2004)132<0749:TVDAOG>2.0.CO;2, 2004.
Curry, J. A., Bentamy, A., Bourassa, M., Bourras, D., Bradley, E., Brunke,
M., Castro, S., Chou, S., Clayson, C., Emery, W., Eymard, L., Cairall, C.,
Kubota, M., Lin, B., Perrie, W., Reeder, R., Renfrew, I., Rossow, W.,
Schulz, J., Smith, S., Webster, P., Wick, G., and Zeng, X.: Seaflux, B.
Am. Meteorol. Soc., 85, 409–424, https://doi.org/10.1175/BAMS-85-3-409, 2004.
De Pondeca, M., Manikin, G. S., Parrish, D. F., Purser, R. J., Wu, W. S.,
DiMego, G., Derber, J. C., Benjamin, S., Horel, J. D., Anderson, L., and
Colman, B.: The status of the real-time mesoscale analysis at NCEP,
Preprints of the 22nd Conference on Weather Analysis and Forecasting/18th
Conference on Numerical Weather Prediction, 24–29 June 2007, Park City, UT, USA, 4A.5, http://ams.confex.com/ams/pdfpapers/124364.pdf (last access: 7 July 2022), 2007.
Edson, J. B., Jampana, V., Weller, R. A., Bigorre, S. P., Plueddemann, A.
J., Fairall, C. W., Miller, S. D., Mahrt, L., Vickers, D., and Hersbach, H.:
On the Exchange of Momentum over the Open Ocean, J. Phys. Oceanogr., 43,
1589–1610, https://doi.org/10.1175/JPO-D-12-0173.1, 2013.
English, J. M., Kren, A. C., and Peevey, T. R.: Improving winter storm
forecasts with observing system simulation experiments (OSSEs). Part II:
Evaluating a satellite gap with idealized and targeted dropsondes, Earth
Space Sci., 5, 176–196, https://doi.org/10.1002/2017EA000350, 2018.
FEMA: FEMA disaster declarations summaries, FEMA [data set],
https://www.fema.gov/api/open/v2/DisasterDeclarationsSummaries (last access: 7 July 2022), 2021.
Fillion, L., Tanguay, M., Lapalme, E., Denis, B., Desgagne, M., Lee, V., Ek,
N., Liu, Z., Lajoie, M., Caron, J., and Pagé, C.: The Canadian Regional
Data Assimilation and Forecasting System, Weather Forecast., 25, 1645–1669,
https://doi.org/10.1175/2010WAF2222401.1, 2010.
Garvert, M., Woods, C., Colle, B., Mass, C., Hobbs, P., Stoelinga, M., and
Wolfe, B.: The 13–14 December 2001 IMPROVE-2 Event. Part II: Comparisons of
MM5 Model Simulations of Clouds and Precipitation with Observations, J.
Atmos. Sci., 62, 3520–3534, https://doi.org/10.1175/JAS3551.1, 2005.
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, 7373–7392, https://doi.org/10.5194/acp-20-7373-2020, 2020.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Hamill, T., Yang, F., Cardinali, C., and Majumdar, S.: Impact of Targeted
Winter Storm Reconnaissance Dropwindsonde Data on Midlatitude Numerical
Weather Predictions, Mon. Weather Rev., 141, 2058–2065,
https://doi.org/10.1175/MWR-D-12-00309.1, 2013.
Hartung, D. C., Otkin, J. A., Peterson, R. A., Turner, D. D., and Feltz, W.
F.: Assimilation of surface-based boundary-layer profiler observations
during a cool season observation system simulation experiment. Part II:
Forecast assessment, Mon. Weather Rev., 139, 2327–2346,
https://doi.org/10.1175/2011MWR3623.1, 2011.
Hou, A. Y., Kakar R., Neeck, S., Azarbarzin, A., Kummerow, C., Kojima, M.,
Oki, R., Nakamura, K., and Iguchi, T.: The Global Precipitation Measurement
mission. B. Am. Meteorol. Soc., 95, 701–722,
https://doi.org/10.1175/BAMS-D-13-00164.1, 2014.
Hu, M., Ge, G., Shao, H., Stark, D., Newman, K., Zhou, C., Beck, J., and
Zhang, X.: Grid-point Statistical Interpolation (GSI) User's Guide Version
3.6, NOAA Developmental Testbed Center, 150 pp.,
https://dtcenter.ucar.edu/com-GSI/users/docs/users_guide/GSIUserGuide_v3.6.pdf (last access: 7 July 2022), 2016.
Hu, M., Ge, G., Zhou, C., Stark, D., Shao, H., Newman, K., Beck, J., and Zhang, X.: Grid-point Statistical Interpolation (GSI) User's Guide Version 3.7, NOAA Developmental Testbed Center, 149 pp., https://dtcenter.org/sites/default/files/GSIUserGuide_v3.7_0.pdf (last access: 11 July 2022), 2018.
Jackson, D. L., Wick, G., and Bates, J.: Near-surface retrieval of air
temperature and specific humidity using multisensor microwave satellite
observations, J. Geophys. Res., 111, D10306, https://doi.org/10.1029/2005JD006431,
2006.
Janjic, Z. I.: The step-mountain eta coordinate model: further developments
of the convection, viscous sublayer and turbulence closure schemes, Mon. Weather Rev., 122, 927–945,
https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2, 1994.
Kain, J., Goss, S., and Baldwin, M.: The Melting Effect as a Factor in
Precipitation-Type Forecasting, Weather Forecast., 15, 700–714,
https://doi.org/10.1175/1520-0434(2000)015<0700:TMEAAF>2.0.CO;2, 2000.
Kim, S., Kim, H. M., Kim, E.-J., and Shin, H.-C.: Forecast sensitivity to
observations for high-impact weather events in the Korean Peninsula,
Atmosphere, 23, 171–186, https://doi.org/10.14191/Atmos.2013.23.2.171, 2013 (in Korean with English abstract).
Kim, S.-M. and Kim, H. M.: Adjoint-based observation impact of Advanced
Microwave Sounding Unit-A (AMSU-A) on the short-range forecasts in East Asia, Atmosphere, 27, 93–104, https://doi.org/10.14191/Atmos.2017.27.1.093, 2017 (in Korean with English abstract).
Kleist, D. T., Parrish, D. F., Derber, J. C., Treadon, R., Wu, W.-S., and
Lord, S.: Introduction of the GSI into the NCEPs Global Data Assimilation
System, Weather Forecast., 24, 1691–1705,
https://doi.org/10.1175/2009WAF2222201.1, 2009.
Lee, H. S. and Yamashita, T.: On the wintertime abnormal storm waves along
the east coast of Korea, in: Asian and Pacific Coasts 2011, World
Scientific, Hong Kong, 1592–1599, https://doi.org/10.1142/9789814366489_0191, 2011.
Lee, J., Son, S.-W., Cho, H.-O., Kim, J., Cha, D.-H., Gyakum, J. R., and
Chen, D.: Extratropical cyclones over East Asia: climatology, seasonal
cycle, and long-term trend, Clim. Dynam., 54, 1131–1144, https://doi.org/10.1007/s00382-019-05048-w, 2020.
Lee, Y.-Y., Lim, G.-H., and Kug, J.-S.: Influence of the East Asian winter
monsoon on the storm track activity over the North Pacific, J. Geophys. Res.-Atmos., 115, D09102, https://doi.org/10.1029/2009JD012813, 2010.
Mitnik, L. M., Gurvich, I. A., and Pichugin, M. K.: Satellite sensing of
intense winter mesocyclones over the Japan Sea, 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), 24–29 July 2011, Vancouver, BC, Canada, Institute of Electrical and Electronics Engineers (IEEE), 2345–2348, https://doi.org/10.1109/IGARSS.2011.6049680, 2011.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of cloud microphysics
on the development of trailing stratiform precipitation in a simulated
squall line: Comparison of one- and two-moment schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP ADP Global Upper Air and Surface Weather Observations (PREPBUFR format),
Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, CO [data set], https://doi.org/10.5065/Z83F-N512, 2008.
Niziol, T. A., Snyder, W. R., and Waldstreicher, J. S.: Winter weather
forecasting throughout the eastern United States. Part IV: Lake effect snow,
Weather Forecast., 10, 61–77,
https://doi.org/10.1175/1520-0434(1995)010<0061:WWFTTE>2.0.CO;2, 1995.
Novak, D. and Colle, B.: Diagnosing Snowband Predictability Using a
Multimodel Ensemble System, Weather Forecast., 27, 565–585,
https://doi.org/10.1175/WAF-D-11-00047.1, 2012.
Novak, D. R., Brill, K. F., and Hogsett, W. A.: Using percentiles to
communicate snowfall uncertainty, Weather Forecast., 29, 1259–1265,
https://doi.org/10.1175/WAF-D-14-00019.1, 2014.
Oh, S.-H. and Jeong, W.-M.: Extensive monitoring and intensive analysis of
extreme winter-season wave events on the Korean east coast, J. Coastal
Research, 70, 296–301, https://doi.org/10.2112/SI70-050.1, 2014.
O'Hara, B., Kaplan, M., and Underwood, S.: Synoptic Climatological Analyses
of Extreme Snowfalls in the Sierra Nevada, Weather Forecast., 24, 1610–1624,
https://doi.org/10.1175/2009WAF2222249.1, 2009.
Parrish, D. F. and Derber, J.: The National Meteorological Center's spectral statistical interpolation analysis system, Mon. Weather Rev., 120, 1747–1763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2, 1992.
Peevey, T. R., English, J. M., Cucurull, L., Wang, H., and Kren, A.
C.: Improving winter storm forecasts with observing system simulation
experiments (OSSEs). Part I: An idealized case study of three U.S.
storms, Mon. Weather Rev., 146, 1341–1366, https://doi.org/10.1175/MWR-D-17-0160.1, 2018.
Petersen, W., Wolff, D., Chandrasekar, V., Roberts, J., and Case, J.: NASA
Observations and Modeling during ICE-POP, KMA ICE-POP Meeting,
27–30 November 2018, Seoul, Republic of Korea,
https://ntrs.nasa.gov/api/citations/20190001414/downloads/20190001414.pdf?attachment=true (last access: 7 July 2022), 2018.
Powers, J. G., Klemp, J., Skamarock, W., Davis, C., Dudhia, J., Gill, D.,
Coen, J., Gochis, D., Ahmadov, R., Peckham, S., Grell, G., Michalakes, J.,
Trahan, S., Benjamin, S., Alexander, C., Dimego, G., Wang, W., Schwartz, C.,
Romine, G., Liu, Z., Snyder, C., Chen, F., Barlage, M., Yu, W., and Duda,
M.: The weather research and forecasting model: Overview, system efforts,
and future directions, B. Am. Meteorol. Soc., 98, 1717–1737,
https://doi.org/10.1175/BAMS-D-15-00308.1, 2017 (code available at: https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html, last access: 7 July 2022).
Ralph, F., Rauber, R., Jewett, B., Kingsmill, D., Pisano, P., Pugner, P.,
Rasmussen, R., Reynolds, D., Schlatter, T., Stewart, R., Tracton, S., and
Waldstreicher, J.: Improving Short-Term (0–48 h) Cool-Season Quantitative
Precipitation Forecasting: Recommendations from a USWRP Workshop, B.
Am. Meteorol. Soc., 86, 1619–1632, https://doi.org/10.1175/BAMS-86-11-1619, 2005.
Ralph, F. M., Sukovich, E., Reynolds, D., Dettinger, M., Weagle, S., Clark, W., and Neiman, P. J.: Assessment of extreme quantitative Precipitation Forecasts and Development of Regional Extreme Event Thresholds Using Data from HMT-2006 and COOP Observers, J. Hydrometeorol., 11, 1286–1304, https://doi.org/10.1175/2010JHM1232.1, 2010.
Roberts, J. B.: GPM Ground Validation SEA FLUX ICE POP, NASA Global Hydrology Resource Center DAAC, Huntsville, AL, USA [data set], https://doi.org/10.5067/GPMGV/ICEPOP/SEAFLUX/DATA101, 2020.
Roberts, J. B., Clayson, C. A., Robertson, F. R., and Jackson, D.
L.: Predicting near-surface atmospheric variables from Special Sensor
Microwave/Imager using neural networks with a first-guess approach, J.
Geophys. Res., 115, D19113, https://doi.org/10.1029/2009JD013099, 2010.
Roller, C., Qian, J.-H., Agel, L., Barlow, M., and Moron, V.: Winter Weather
Regimes in the Northeast United States, J. Climate, 29, 2963–2980,
https://doi.org/10.1175/JCLI-D-15-0274.1, 2016.
Ryu, S., Song, J. J., Kim, Y., Jung, S.-H., Do, Y., and Lee, G.: Spatial
Interpolation of Gauge Measured Rainfall Using Compressed Sensing,
Asia-Pac. J. Atmos. Sci., 57, 331–345,
https://doi.org/10.1007/s13143-020-00200-7, 2020.
Saslo, S. and Greybush, S. J.: Prediction of lake-effect snow using
convection-allowing ensemble forecasts and regional data assimilation, Weather Forecast., 32, 1727–1744, https://doi.org/10.1175/WAF-D-16-0206.1, 2017.
Schultz, D. M., Steenburgh, W. J., Trapp, R. J., Horel, J., Kingsmill, D.
E., Dunn, L. B., Rust, W. D., Cheng, L., Bansemer, A., Cox, J., Daugherty,
J., Jorgensen, D. P., Meitín, J., Showell, L., Smull, B. F., Tarp, K.,
and Trainor, M.: Understanding Utah winter storms: The Intermountain
Precipitation Experiment, B. Am. Meteorol. Soc., 83, 189–210,
https://doi.org/10.1175/1520-0477(2002)083<0189:UUWSTI>2.3.CO;2, 2002.
Schuur, T., Park, H.-S., Ryzhkov, A., and Reeves, H.: Classification of
Precipitation Types during Transitional Winter Weather Using the RUC Model
and Polarimetric Radar Retrievals, J. Appl. Meteorol. Clim., 51, 763–779,
https://doi.org/10.1175/JAMC-D-11-091.1, 2012.
Skofronick-Jackson, G., Petersen, W., Berg, W., Kidd, C., Stocker, E.,
Kirschbaum, D., Kakar, R., Braun, S., Huffman, G., Iguchi, T., Kirstetter,
P., Kummerow, C., Meneghini, R., Oki, R., Olson, W., Takayabu, Y., Furukawa,
K., and Wilheit, T.: The Global Precipitation Measurement (GPM) mission for
science and society, B. Am. Meteorol. Soc., 98, 1679–1695,
https://doi.org/10.1175/BAMS-D-15-00306.1, 2017.
Smith, A. B.: U.S. Billion-dollar Weather and Climate Disasters, 1980–present (NCEI Accession 0209268), NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/stkw-7w73, 2020.
Tomita, H., Hihara, T., and Kubota, M.: Improved Satellite Estimation of
Near-Surface Humidity Using Vertical Water Vapor Profile Information,
Geophys. Res. Lett., 45, 899–906, https://doi.org/10.1002/2017GL076384, 2018.
Tsuboki, K. and Asai, T.: The multi-scale structure and development
mechanism of mesoscale cyclones over the Sea of Japan in winter, J. Meteorol.
Soc. Jpn., 82, 597–621, https://doi.org/10.2151/jmsj.2004.597, 2004.
Wu, W.-S.: Background error for NCEP's GSI analysis in regional mode, Proceeding of the 4th International Symposium on Analysis of Observations in Meteorology and Oceanography, 18–22 April 2005, Prague, Czech Republic, WMO, WWRP 9, WMO-TD 1316, CD-ROM, 2005.
Wu, W.-S., Parrish, D. F., and Purser, R. J.: Three-dimensional variational
analysis with spatially inhomogeneous covariances, Mon. Weather Rev., 130,
2905–2916, https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2, 2002.
Xiao, Q., Kuo, Y. H., Sun, J., Lee, W. C., Lim, E., Guo, Y. R., and Barker,
D. M.: Assimilation of Doppler Radar Observations with a Regional 3DVAR
System: Impact of Doppler Velocities on Forecasts of a Heavy Rainfall Case,
J. Appl. Meteor., 44, 768–788, https://doi.org/10.1175/JAM2248.1, 2005.
Yang, E.-G. and Kim, H. M.: A comparison of variational, ensemble-based,
and hybrid data assimilation methods over East Asia for two one-month
periods, Atmos. Res., 249, 105257, https://doi.org/10.1016/j.atmosres.2020.105257, 2021.
Yoshiike, S. and Kawamura, R.: Influence of wintertime large-scale
circulation on the explosively developing cyclones over the western North
Pacific and their downstream effects, J. Geophys. Res.-Atmos., 114, D13110,
https://doi.org/10.1029/2009JD011820, 2009.
Zhang, F., Meng, Z., and Aksoy, A.: Tests of an Ensemble Kalman Filter for
Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model
Experiments, Mon. Weather Rev., 134, 722–736,
https://doi.org/10.1175/MWR3101.1, 2006.
Zhang, F., Sun, Y. Q., Magnusson, L., Buizza, R., Lin, S.-J., Chen, J.-H.,
and Emanuel, K.: What is the predictability limit of midlatitude weather?, J.
Atmos. Sci., 76, 1077–1091, https://doi.org/10.1175/JAS-D-18-0269.1, 2019.
Zhang, Y., Sperber, K. R., and Boyle, J. S.: Climatology and interannual
variation of the East Asian Winter Monsoon: Results from the 1979–95
NCEP/NCAR reanalysis, Mon. Weather Rev., 125, 2605–2619, https://doi.org/10.1175/1520-0493(1997)125<2605:CAIVOT>2.0.CO;2, 1997.
Zhang, Y., Ding, Y., and Li, Q.: A climatology of extratropical cyclones
over East Asia during 1958–2001, Acta. Meteorol. Sin., 26, 261–277,
https://doi.org/10.1007/s13351-012-0301-2, 2012.
Zupanski, M., Zupanski, D., Parrish, D., Rogers, E., and DiMego, G.:
Four-Dimensional Variational Data Assimilation for the Blizzard of 2000,
Mon. Weather Rev., 130, 1967–1988,
https://doi.org/10.1175/1520-0493(2002)130<1967:FDVDAF>2.0.CO;2, 2002.
Short summary
This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean surface meteorology data into the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system. This was for two snowstorms during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field experiments. The results indicated a positive impact of the data for short-term forecasts for heavy snowfall.
This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean...
