Data assimilation of radar reflectivity volumes in a LETKF scheme

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.

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Data assimilation of radar reflectivity volumes in a LETKF scheme

10.5194/npg-2018-27 ◽

2018 ◽

Author(s):

Thomas Gastaldo ◽

Virginia Poli ◽

Chiara Marsigli ◽

Pier Paolo Alberoni ◽

Tiziana Paccagnella

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Radar Reflectivity ◽

Precipitation Forecast ◽

Small Scale ◽

Test Case ◽

Observational Error ◽

Structure Amplitude ◽

Emilia Romagna Region ◽

The Impact

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Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

Remote Sensing ◽

10.3390/rs13112103 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2103

Author(s):

Yuchen Liu ◽

Jia Liu ◽

Chuanzhe Li ◽

Fuliang Yu ◽

Wei Wang

Keyword(s):

Data Assimilation ◽

Northern China ◽

Horizontal Resolution ◽

Radar Reflectivity ◽

Small Scale ◽

Time Interval ◽

Outer Domain ◽

Storm Prediction ◽

The Impact ◽

Assimilation Time

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

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

Advances in Science and Research ◽

10.5194/asr-14-187-2017 ◽

2017 ◽

Vol 14 ◽

pp. 187-194 ◽

Cited By ~ 5

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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Numerical simulations of June 7, 2020 convective precipitation over Slovakia using deterministic, probabilistic, and convection-permitting approaches

Időjárás ◽

10.28974/idojaras.2021.4.3 ◽

2021 ◽

Vol 125 (4) ◽

pp. 571-607

Author(s):

André Simon ◽

Martin Belluš ◽

Katarína Čatlošová ◽

Mária Derková ◽

Martin Dian ◽

...

Keyword(s):

Satellite Imagery ◽

Weather Prediction ◽

Severe Weather ◽

Precipitation Forecast ◽

Convective Precipitation ◽

Small Scale ◽

Limited Area ◽

Systematic Effect ◽

The Impact

The paper presented is dedicated to the evaluation of the influence of various improvements to the numerical weather prediction (NWP) systems exploited at the Slovak Hydrometeorological Institute (SHMÚ). The impact was illustrated in a case study with multicell thunderstorms and the results were confronted with the reference analyses from the INCA nowcasting system, regional radar reflectivity data, and METEOSAT satellite imagery. The convective cells evolution was diagnosed in non-hydrostatic dynamics experiments to study weak mesoscale vortices and updrafts. The growth of simulated clouds and evolution of the temperature at their top were compared with the brightness temperature analyzed from satellite imagery. The results obtained indicated the potential for modeling and diagnostics of small-scale structures within the convective cloudiness, which could be related to severe weather. Furthermore, the non-hydrostatic dynamics experiments related to the stability and performance improvement of the time scheme led to the formulation of a new approach to linear operator definition for semi-implicit scheme (in text referred as NHHY). We demonstrate that the execution efficiency has improved by more than 20%. The exploitation of several high resolution measurement types in data assimilation contributed to more precise position of predicted patterns and precipitation representation in the case study. The non-hydrostatic dynamics provided more detailed structures. On the other hand, the potential of a single deterministic forecast of prefrontal heavy precipitation was not as high as provided by the ensemble system. The prediction of a regional ensemble system A-LAEF (ALARO Limited Area Ensemble Forecast) enhanced the localization of precipitation patterns. Though, this was rather due to the simulation of uncertainty in the initial conditions and also because of the stochastic perturbation of physics tendencies. The various physical parameterization setups of A-LAEF members did not exhibit a systematic effect on precipitation forecast in the evaluated case. Moreover, the ensemble system allowed an estimation of uncertainty in a rapidly developing severe weather case, which was high even at very short range.

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Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

Applied Sciences ◽

10.3390/app10165493 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5493 ◽

Cited By ~ 1

Author(s):

Jingnan Wang ◽

Lifeng Zhang ◽

Jiping Guan ◽

Mingyang Zhang

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Weather Forecast ◽

Radar Data ◽

Precipitation Forecast ◽

Rainfall Forecast ◽

Skill Scores ◽

The Individual ◽

The Impact ◽

Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

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Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

Nonlinear Processes in Geophysics ◽

10.5194/npg-21-1027-2014 ◽

2014 ◽

Vol 21 (5) ◽

pp. 1027-1041 ◽

Cited By ~ 6

Author(s):

K. Apodaca ◽

M. Zupanski ◽

M. DeMaria ◽

J. A. Knaff ◽

L. D. Grasso

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

New Information ◽

Forecasting System ◽

Maximum Likelihood Ensemble Filter ◽

The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

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

Monthly Weather Review ◽

10.1175/mwr-d-20-0174.1 ◽

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.

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

Weather and Forecasting ◽

10.1175/waf-d-16-0202.1 ◽

2017 ◽

Vol 32 (4) ◽

pp. 1603-1611 ◽

Cited By ~ 3

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.

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Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

Nonlinear Processes in Geophysics Discussions ◽

10.5194/npgd-1-917-2014 ◽

2014 ◽

Vol 1 (1) ◽

pp. 917-952

Author(s):

K. Apodaca ◽

M. Zupanski ◽

M. DeMaria ◽

J. A. Knaff ◽

L. D. Grasso

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

New Information ◽

Forecasting System ◽

Maximum Likelihood Ensemble Filter ◽

The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

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

10.5194/gmd-2021-161 ◽

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.

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