scholarly journals Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

2020 ◽  
Vol 12 (3) ◽  
pp. 383 ◽  
Author(s):  
Christos Giannaros ◽  
Vassiliki Kotroni ◽  
Konstantinos Lagouvardos ◽  
Theodore M. Giannaros ◽  
Christos Pikridas
Keyword(s):  
Data Assimilation ◽  
Time Window ◽  
Positive Impact ◽  
High Impact ◽  
Global Navigation Satellite Systems ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Tropospheric Delay ◽  
The Impact ◽  
Precipitation Events

The derivation of global navigation satellite systems (GNSSs) tropospheric products is nowadays a state-of-the-art technique that serves both research and operational needs in a broad range of applications in meteorology. In particular, GNSS zenith tropospheric delay (ZTD) data assimilation is widely applied in Europe to enhance numerical weather predictions (NWPs). The current study presents the first attempt at introducing assimilation of ZTDs, derived from more than 48 stations of the Hellenic GNSS network, into the operational NWP system of the National Observatory of Athens (NOA) in Greece, which is based on the mesoscale Weather Research and Forecasting (WRF) model. WRF was applied during seven high-impact precipitation events covering the dry and wet season of 2018. The simulation employing the ZTD data assimilation reproduces more accurately, compared to the control experiment, the observed heavy rainfall (especially for high precipitation events, exceeding 20 mm in 24h) during both dry and wet periods. Assimilating ZTDs also improves the simulation of intense (>20 mm) convective precipitation during the time window of its occurrence in the dry season, and provides a beneficial influence during synoptic-scale events in the wet period. The above results, which are statistically significant, highlight an important positive impact of ZTD assimilation on the model’s precipitation forecast skill over Greece. Overall, the modelling system’s configuration, including the assimilation of ZTD observations, satisfactorily captures the spatial and temporal distribution of the observed rainfall and can therefore be used as the basis for examining further improvements in the future.

scholarly journals Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Tuanjie Hou ◽  
Fanyou Kong ◽  
Xunlai Chen ◽  
Hengchi Lei
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Positive Impact ◽  
Southern China ◽  
Precipitation Forecast ◽  
Radiosonde Data ◽  
Near Surface ◽  
Rainfall Events ◽  
Forecasting System ◽  
The Impact

This study examines the impact of three-dimensional variational data assimilation (3DVAR) on the prediction of two heavy rainfall events over Southern China by using a real-time storm-scale forecasting system. Initialized from the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution data, the forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) 3DVAR package. Observations from Doppler radars, surface Automatic Weather Station (AWS) network, and radiosondes are used in the experiments to evaluate the impact of data assimilation on short-term quantitative precipitation forecast (QPF) skill. Results suggest that extrasurface AWS data assimilation has slight but general positive impact on rainfall location forecasts. Surface AWS data also improve model results of near-surface variables. Radiosonde data assimilation improves the QPF skill by improving rainfall position accuracy and reducing rainfall overprediction. Compared with radar data, the overall impact of additional surface and radiosonde data is smaller and is reflected primarily in reducing rainfall overestimation. The assimilation of all radar, surface, and radiosonde data has a more positive impact on the forecast skill than the assimilation of either type of data only for the two rainfall events.

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 Impact of Lightning Data Assimilation on the Short-Term Precipitation Forecast over the Central Mediterranean Sea

2021 ◽  
Vol 13 (4) ◽  
pp. 682
Author(s):  
Rosa Claudia Torcasio ◽  
Stefano Federico ◽  
Albert Comellas Prat ◽  
Giulia Panegrossi ◽  
Leo Pio D'Adderio ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Mediterranean Sea ◽  
Positive Impact ◽  
Weather Forecast ◽  
Precipitation Forecast ◽  
Time Interval ◽  
Precipitation Pattern ◽  
Central Mediterranean ◽  
Central Mediterranean Sea ◽  
The Impact

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of convective events and has been widely applied with this purpose in the past two decades. Most of these applications refer to events hitting coastal and land areas, where people live. However, a weather forecast over the sea has many important practical applications, and this paper focuses on the impact of LDA on the precipitation forecast over the central Mediterranean Sea around Italy. The 3 h rapid update cycle (RUC) configuration of the weather research and forecasting (WRF) model) has been used to simulate the whole month of November 2019. Two sets of forecasts have been considered: CTRL, without lightning data assimilation, and LIGHT, which assimilates data from the LIghtning detection NETwork (LINET). The 3 h precipitation forecast has been compared with observations of the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) (IMERG) dataset and with rain gauge observations recorded in six small Italian islands. The comparison of CTRL and LIGHT precipitation forecasts with the IMERG dataset shows a positive impact of LDA. The correlation between predicted and observed precipitation improves over wide areas of the Ionian and Adriatic Seas when LDA is applied. Specifically, the correlation coefficient for the whole domain increases from 0.59 to 0.67, and the anomaly correlation (AC) improves by 5% over land and by 8% over the sea when lightning is assimilated. The impact of LDA on the 3 h precipitation forecast over six small islands is also positive. LDA improves the forecast by both decreasing the false alarms and increasing the hits of the precipitation forecast, although with variability among the islands. The case study of 12 November 2019 (time interval 00–03 UTC) has been used to show how important the impact of LDA can be in practice. In particular, the shifting of the main precipitation pattern from land to the sea caused by LDA gives a much better representation of the precipitation field observed by the IMERG precipitation product.

scholarly journals Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator

2019 ◽  
Vol 12 (9) ◽  
pp. 4829-4848 ◽  
Author(s):  
Natalia Hanna ◽  
Estera Trzcina ◽  
Gregor Möller ◽  
Witold Rohm ◽  
Robert Weber
Keyword(s):  
Standard Deviation ◽  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Heavy Precipitation ◽  
Global Navigation Satellite Systems ◽  
Weather Research And Forecasting ◽  
The Impact ◽  
Precipitation Events ◽  
Weather Research

Abstract. From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3-D grid of voxels, and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting, the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of central Europe during the period of 29 May–14 June 2013, when heavy-precipitation events were observed. For both models, slant wet delays (SWDs) were calculated based on estimates of zenith total delay (ZTD) and horizontal gradients, provided for 88 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model), in order to assess the impact of different factors on the tomographic solution. The GNSS tomography outputs have been assimilated into the nested (12 and 36 km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3D-Var) system, in particular, its radio occultation observation operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy-precipitation events. Future improvements to the assimilation method are also discussed.

scholarly journals Impact of high-resolution data assimilation of GPS zenith delay on Mediterranean heavy rainfall forecasting

2009 ◽  
Vol 27 (7) ◽  
pp. 2739-2753 ◽  
Author(s):  
K. Boniface ◽  
V. Ducrocq ◽  
G. Jaubert ◽  
X. Yan ◽  
P. Brousseau ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Good Description ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Tropospheric Delay ◽  
Gps Data ◽  
Convective System ◽  
First Case

Abstract. Impact of GPS (Global Positioning System) data assimilation is assessed here using a high-resolution numerical weather prediction system at 2.5 km horizontal resolution. The Zenithal Tropospheric Delay (ZTD) GPS data from mesoscale networks are assimilated with the 3DVAR AROME data assimilation scheme. Data from more than 280 stations over the model domain have been assimilated during 15-day long assimilation cycles prior each of the two studied events. The results of these assimilation cycles show that the assimilation of GPS ZTD with the AROME system performs well in producing analyses closer to the ZTD observations in average. Then the impacts of assimilating GPS data on the precipitation forecast have been evaluated. For the first case, only the AROME runs starting a few hours prior the triggering of the convective system are able to simulate the convective precipitation. The assimilation of GPS ZTD observations improves the simulation of the spatial extent of the precipitation, but slightly underestimates the heaviest precipitation in that case compared with the experiment without GPS. The accuracy of the precipitation forecast for the second case is much better. The analyses from the control assimilation cycle provide already a good description of the atmosphere state that cannot be further improved by the assimilation of GPS observations. Only for the latest day (22 November 2007), significant differences have been found between the two parallel cycles. In that case, the assimilation of GPS ZTD allows to improve the first 6 to 12 h of the precipitation forecast.

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.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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

Időjárás ◽  
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.

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

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
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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