scholarly journals Assimilation of GPM-retrieved Ocean Surface Meteorology Data for Two Snowstorm Events during ICE-POP 2018

2021 ◽  
Author(s):  
Xuanli Li ◽  
Jason B. Roberts ◽  
Jayanthi Srikishen ◽  
Jonathan L. Case ◽  
Walter A. Petersen ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Wrf Model ◽  
Field Research ◽  
Ocean Surface ◽  
Positive Influence ◽  
Precipitation Forecast ◽  
Specific Humidity ◽  
Wind Fields

Abstract. As a component of the National Aeronautics and Space Administration (NASA) Weather Focus Area and GPM Ground Validation participation in the International Collaborative Experiments for 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 Global Precipitation Measurement (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 to explore the application of these observations for two heavy snowfall events during the ICE-POP 2018: 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, positive influence of the retrieved surface observation was found for the March case with improved quantitative precipitation forecast and reduced error in temperature forecast. A slightly smaller yet positive impact was also found in the forecast of the February case.

Improved impacts in observing system simulation experiments of radio occultation observations as a result of model and data assimilation changes

2020 ◽  
Author(s):  
L. CUCURULL ◽  
S. P. F. CASEY
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Positive Impact ◽  
System Simulation ◽  
Weather Prediction ◽  
Wind Fields ◽  
Simulation Experiments ◽  
The Impact ◽  
Forward Operator

AbstractAs global data assimilation systems continue to evolve, Observing System Simulation Experiments (OSSEs) need to be updated to accurately quantify the impact of proposed observing technologies in weather forecasting. Earlier OSSEs with radio occultation (RO) observations have been updated and the impact of the originally proposed Constellation Observing Satellites for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) mission, with a high-inclination and low-inclination component, has been investigated by using the operational data assimilation system at NOAA and a 1-dimensional bending angle RO forward operator. It is found that the impact of the low-inclination component of the originally planned COSMIC-2 mission (now officially named COSMIC-2) has significantly increased as compared to earlier studies, and significant positive impact is now found globally in terms of mass and wind fields. These are encouraging results as COSMIC-2 was successfully launched in June 2019 and data have been recently released to operational weather centers. Earlier findings remain valid indicating that globally distributed RO observations are more important to improve weather prediction globally than a denser sampling of the tropical latitudes. Overall, the benefits reported here from assimilating RO soundings are much more significant than the impacts found in previous OSSEs. This is largely attributed to changes in the data assimilation and forecast system and less to the more advanced 1-dimensional forward operator chosen for the assimilation of RO observations.

Forecast Impact of Assimilating Aircraft WVSS-II Water Vapor Mixing Ratio Observations in the Global Data Assimilation System (GDAS)

2017 ◽  
Vol 32 (4) ◽  
pp. 1603-1611 ◽  
Author(s):  
Brett T. Hoover ◽  
David A. Santek ◽  
Anne-Sophie Daloz ◽  
Yafang Zhong ◽  
Richard Dworak ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Global Data Assimilation System ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Abstract Automated aircraft observations of wind and temperature have demonstrated positive impact on numerical weather prediction since the mid-1980s. With the advent of the Water Vapor Sensing System (WVSS-II) humidity sensor, the expanding fleet of commercial aircraft with onboard automated sensors is also capable of delivering high quality moisture observations, providing vertical profiles of moisture as aircraft ascend out of and descend into airports across the continental United States. Observations from the WVSS-II have to date only been monitored within the Global Data Assimilation System (GDAS) without being assimilated. In this study, aircraft moisture observations from the WVSS-II are assimilated into the GDAS, and their impact is assessed in the Global Forecast System (GFS). A two-season study is performed, demonstrating a statistically significant positive impact on both the moisture forecast and the precipitation forecast at short range (12–36 h) during the warm season. No statistically significant impact is observed during the cold season.

scholarly journals Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall

2019 ◽  
Vol 11 (8) ◽  
pp. 973 ◽  
Author(s):  
Yuanbing Wang ◽  
Yaodeng Chen ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Eastern China ◽  
Wrf Model ◽  
Analysis Data ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Precipitation Analysis ◽  
The Impact

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.

scholarly journals Data assimilation of radar reflectivity volumes in a LETKF scheme

2018 ◽  
Vol 25 (4) ◽  
pp. 747-764 ◽  
Author(s):  
Thomas Gastaldo ◽  
Virginia Poli ◽  
Chiara Marsigli ◽  
Pier Paolo Alberoni ◽  
Tiziana Paccagnella
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Small Scale ◽  
Observational Error ◽  
The Impact

Abstract. Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.

scholarly journals A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

2020 ◽  
Vol 12 (8) ◽  
pp. 1243 ◽  
Author(s):  
Xuanli Li ◽  
John R. Mecikalski ◽  
Timothy J. Lang
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Data Assimilation ◽  
Positive Impact ◽  
Specular Reflection ◽  
Surface Wind ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Wind Speed Data ◽  
The Impact

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

scholarly journals Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

2021 ◽  
Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Implementation Process ◽  
Satellite System ◽  
Limited Area ◽  
Global Navigation Satellite ◽  
Microwave Radiances ◽  
Data Assimilation System ◽  
Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

scholarly journals Evaluation of Operational Monsoon Moisture Surveillance and Severe Weather Prediction Utilizing COSMIC-2/FORMOSAT-7 Radio Occultation Observations

2021 ◽  
Vol 13 (15) ◽  
pp. 2979
Author(s):  
Yu-Chun Chen ◽  
Chih-Chien Tsai ◽  
Yi-Chao Wu ◽  
An-Hsiang Wang ◽  
Chieh-Ju Wang ◽  
...  
Keyword(s):  
Numerical Model ◽  
Data Assimilation ◽  
Water Resource Management ◽  
Radio Occultation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Severe Weather ◽  
Model Experiments ◽  
Typhoon Structure ◽  
Numerical Model Experiments

Operational monsoon moisture surveillance and severe weather prediction is essential for timely water resource management and disaster risk reduction. For these purposes, this study suggests a moisture indicator using the COSMIC-2/FORMOSAT-7 radio occultation (RO) observations and evaluates numerical model experiments with RO data assimilation. The RO data quality is validated by a comparison between sampled RO profiles and nearby radiosonde profiles around Taiwan prior to the experiments. The suggested moisture indicator accurately monitors daily moisture variations in the South China Sea and the Bay of Bengal throughout the 2020 monsoon rainy season. For the numerical model experiments, the statistics of 152 moisture and rainfall forecasts for the 2020 Meiyu season in Taiwan show a neutral to slightly positive impact brought by RO data assimilation. A forecast sample with the most significant improvement reveals that both thermodynamic and dynamic fields are appropriately adjusted by model integration posterior to data assimilation. The statistics of 17 track forecasts for typhoon Hagupit (2020) also show the positive effect of RO data assimilation. A forecast sample reveals that the member with RO data assimilation simulates better typhoon structure and intensity than the member without, and the effect can be larger and faster via multi-cycle RO data assimilation.

scholarly journals Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

2019 ◽  
Vol 12 (9) ◽  
pp. 3939-3954
Author(s):  
Frederik Kurzrock ◽  
Hannah Nguyen ◽  
Jerome Sauer ◽  
Fabrice Chane Ming ◽  
Sylvain Cros ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Cloud Water ◽  
Specific Humidity ◽  
Reunion Island ◽  
Short Term ◽  
Water Path ◽  
Réunion Island ◽  
Cloud Water Path ◽  
The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.

Variational Assimilation of Cloud Liquid/Ice Water Path and Its Impact on NWP

2015 ◽  
Vol 54 (8) ◽  
pp. 1809-1825 ◽  
Author(s):  
Yaodeng Chen ◽  
Hongli Wang ◽  
Jinzhong Min ◽  
Xiang-Yu Huang ◽  
Patrick Minnis ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Liquid Water ◽  
Weather Prediction ◽  
Wrf Model ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Water Path ◽  
Ice Water Path ◽  
Ice Water ◽  
The Impact

AbstractAnalysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

scholarly journals Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

2017 ◽  
Vol 14 ◽  
pp. 187-194 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Claudio Transerici ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Time Range ◽  
False Alarms ◽  
Forecast Time ◽  
The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

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