scholarly journals Application of radar data assimilation on convective precipitation forecasts based on water vapor retrieval

2021 ◽  
Author(s):  
Zhixin He ◽  
Dongyong Wang ◽  
Xuexing Qiu ◽  
Yang Jiang ◽  
Huimin Li ◽  
...  
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Middle Layer ◽  
Radar Reflectivity ◽  
Convective Precipitation ◽  
Initial Field ◽  
Short Time ◽  
The Impact ◽  
Reflectivity Factor

AbstractBased on a short-time heavy rainfall in Anhui and the weather research and forecasting (WRF) model, the water vapor in the initial field of the model is retrieved using the statistical relationships of the reflectivity factor from the Doppler weather radar with the relative humidity and hydrometeor. Three-dimensional variational (3DVAR) assimilation method is used to assimilate the radar reflectivity factor and radial velocity, and then the impact of assimilating retrieved water vapor on the analysis and forecast of the torrential rain is assessed. The results show that, after assimilating the retrieved water vapor, the water vapor field in the model is significantly improved. The water vapor content in the middle layer of the model in the analyzed field is increased, corresponding well with the convective region. Meanwhile, the precipitation distribution during this weather process is successfully simulated. The mesoscale characteristics are better presented by the imageries of radar reflectivity factor, and false echoes are partially reduced. Besides, the prediction of short-time heavy rainfall regions is closer to the actual observations. After assimilating the retrieved water vapor, the simulated one-hour accumulated rainfall is closer to the actual observation, and the fraction skill score (FSS) is higher.

scholarly journals On the Diurnal Cycle of GPS-Derived Precipitable Water Vapor over Sumatra

2019 ◽  
Vol 76 (11) ◽  
pp. 3529-3552
Author(s):  
Giuseppe Torri ◽  
David K. Adams ◽  
Huiqun Wang ◽  
Zhiming Kuang
Keyword(s):  
Water Vapor ◽  
Diurnal Cycle ◽  
Wrf Model ◽  
Precipitable Water ◽  
Active Phase ◽  
Precipitable Water Vapor ◽  
Tectonic Activity ◽  
Western Coast ◽  
The Impact ◽  
Spectrometer Data

Abstract Convective processes in the atmosphere over the Maritime Continent and their diurnal cycles have important repercussions for the circulations in the tropics and beyond. In this work, we present a new dataset of precipitable water vapor (PWV) obtained from the Sumatran GPS Array (SuGAr), a dense network of GPS stations principally for examining seismic and tectonic activity along the western coast of Sumatra and several offshore islands. The data provide an opportunity to examine the characteristics of convection over the area in greater detail than before. In particular, our results show that the diurnal cycle of PWV on Sumatra has a single late afternoon peak, while that offshore has both a midday and a nocturnal peak. The SuGAr data are in good agreement with GPS radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission, as well as with imaging spectrometer data from the Ozone Measuring Instrument (OMI). A comparison between SuGAr and the NASA Water Vapor Project (NVAP), however, shows significant differences, most likely due to discrepancies in the temporal and spatial resolutions. To further understand the diurnal cycle contained in the SuGAr data, we explore the impact of the Madden–Julian oscillation (MJO) on the diurnal cycle with the aid of the Weather Research and Forecasting (WRF) Model. Results show that the daily mean and the amplitude of the diurnal cycle appear smaller during the suppressed phase relative to the developing/active MJO phase. Furthermore, the evening/nighttime peaks of PWV offshore appear later during the suppressed phase of the MJO compared to the active phase.

Towards the integration of GNSS, SAR and NWP for heavy rainfall forecast in sub-Saharan Africa within the TWIGA project

2021 ◽  
Author(s):  
Agostino N Meroni ◽  
Alessandra Mascitelli ◽  
Stefano Barindelli ◽  
Naomi Petrushevsky ◽  
Marco Manzoni ◽  
...  
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Atmospheric Dynamics ◽  
Water Vapor Content ◽  
Sustainable Growth ◽  
Sub Saharan Africa ◽  
Synthetic Aperture Radar Data ◽  
Sub Saharan ◽  
The Impact

<p>The H2020 TWIGA - Transforming Weather Water data into value-added Information services for sustainable Growth in Africa - project aims to establish various services in sub-Saharan Africa for a better management of water resources by linking satellite, in-situ and modelled information. The delivery of timely and accurate weather forecasts is one of the envisaged services. GNSS (Global Navigation Satellite Systems) and SAR (Synthetic Aperture Radar) data provide information on the atmospheric water vapor content, which can be assimilated into Numerical Weather Prediction (NWP) models. The assimilation enables these models to exploit observations for a better simulation of the atmospheric dynamics and the subsequent improvement of the forecasts. The activities related to GNSS, SAR and NWP integration are presented in what follows.</p><p>As for GNSS, the modeling of ionospheric errors was investigated for the recently deployed single-frequency low-cost sensors in Uganda. A quality assessment of three different algorithms (ANGBAS, SEID, goSEID) for synthetic L2 observations reconstruction, evaluating the impact on the Zenith Total Delay (ZTD) estimation, was carried out. The three methods show good performances with an overall accuracy ranging between 0.1 and 1 cm when the corrections are computed from geodetic stations at distances up to 65 km from the target receiver. Additionally, an operational system for the retrieval of near real-time GNSS ZTD was implemented. It shows a precision lower than 1 cm, compatible with the target requirements for the assimilation into NWP models.</p><p>GNSS is also used to perform the orbital corrections of the SAR products, reducing the large-scale errors like phase trends and biases. The merging of multiple Sentinel-1 frames to cover extended areas requires large computational resources. Work is ongoing to deal with the computationally intensive unwrapping of large interferograms. Moreover, the removal of ionospheric delays, which are not related to the water vapor content, is under development. </p><p>Concerning NWP, the Weather Research and Forecasting (WRF) model has been used, at cloud-resolving scales, to test the sensitivity of the simulations of three heavy rainfall events (in Uganda and in South Africa) to the Planetary Boundary Layer (PBL) and the microphysical numerical schemes. Non-local PBL schemes are found to outperform the local PBL scheme considered in the study, because they better describe the vertical atmospheric mixing. In parallel, by exploiting a multiphysics set of numerical simulations in West Africa, it was found that the spatial variability of the surface heat fluxes significantly affects the lower atmospheric dynamics. This happens through a differential heating of the atmosphere across soil moisture gradients. Experiments on the assimilation of water vapor data are ongoing.</p>

Very Short Time Range Forecasting Using CReSS-3DVAR for a Meso-γ-Scale, Localized, Extremely Heavy Rainfall Event: Comparison with an Extrapolation-Based Nowcast

2017 ◽  
Vol 12 (5) ◽  
pp. 967-979 ◽  
Author(s):  
Ryohei Kato ◽  
◽  
Shingo Shimizu ◽  
Ken-ichi Shimose ◽  
Koyuru Iwanami
Keyword(s):  
Heavy Rainfall ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Radar Reflectivity ◽  
Heavy Rainfall Event ◽  
Time Range ◽  
Nwp Model ◽  
Short Time

The forecast accuracy of a numerical weather prediction (NWP) model for a very short time range (≤1 h) for a meso-γ-scale (2–20 km) extremely heavy rainfall (MγExHR) event that caused flooding at the Shibuya railway station in Tokyo, Japan on 24 July 2015 was compared with that of an extrapolation-based nowcast (EXT). The NWP model used CReSS with 0.7 km horizontal grid spacing, and storm-scale data from dense observation networks (radars, lidars, and microwave radiometers) were assimilated using CReSS-3DVAR. The forecast accuracy of the heavy rainfall area (≥20 mm h-1), as a function of forecast time (FT), was investigated for the NWP model and EXT predictions using the fractions skill score (FSS) for various spatial scales of displacement error (L). These predictions were started 30 minutes before the onset of extremely heavy rainfall at Shibuya station. The FSS for L=1 km, i.e., grid-scale verification, showed NWP accuracy was lower than that of EXT before FT=40 min; however, NWP accuracy surpassed that of EXT from FT=45 to 60 min. This suggests the possibility of seamless, high-accuracy forecasts of heavy rainfall (≥20 mm h-1) associated with MγExHR events within a very short time range (≤1 h) by blending EXT and NWP outputs. The factors behind the fact that the NWP model predicted heavy rainfall area within the very short time range of ≤1 h more correctly than did EXT are also discussed. To enable this discussion of the factors, additional sensitivity experiments with a different assimilation method of radar reflectivity were performed. It was found that a moisture adjustment above the lifting condensation level using radar reflectivity was critical to the forecasting of heavy rainfall near Shibuya station after 25 min.

scholarly journals Impact of Kalpana-1-Derived Water Vapor Winds on Indian Ocean Tropical Cyclone Forecasts

2010 ◽  
Vol 138 (3) ◽  
pp. 987-1003 ◽  
Author(s):  
S. K. Deb ◽  
C. M. Kishtawal ◽  
P. K. Pal
Keyword(s):  
Water Vapor ◽  
Indian Ocean ◽  
Wrf Model ◽  
Initial Position ◽  
Space Applications ◽  
Sea Level Pressure ◽  
Position Errors ◽  
Ocean Region ◽  
Wrf Modeling ◽  
The Impact

Abstract The water vapor winds from the operational geostationary Indian National Satellite (INSAT) Kalpana-1 have recently become operational at the Space Applications Centre (SAC). A series of experimental forecasts are attempted here to evaluate the impact of water vapor winds derived from Kalpana-1 for the track and intensity prediction of two Bay of Bengal tropical cyclones (TCs), Sidr and Nargis, using the Weather Research and Forecasting (WRF) modeling system. The assimilation of water vapor winds has made some impact in the initial position errors as well as track forecasts when compared with the corresponding control experiments for both TCs. However, no statistically significant improvement is noticed in the simulations of TC intensities [i.e., minimum sea level pressure (MSLP) and maximum surface winds forecasts when satellite winds are used for assimilation]. Moreover, the performance of Kalpana-1 winds is evaluated by repeating the same sets of experiments using Meteosat-7 winds derived at the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and compared with observed data. The simulation of initial position errors, and track and intensity forecasts using the assimilation of water vapor winds from both satellites are comparable. Though, these results are preliminary with respect to the Kalpana-1 winds, the present study can provide some insight to the WRF model users over the Indian Ocean region.

scholarly journals An Experiment for Assimilating Different Type of Data Observations in Forecasting Heavy Rainfall over Central Highlands Region Due to the Impact of Hurricane Damrey

2019 ◽  
Vol 35 (4) ◽  
Author(s):  
Le Lan Phuong ◽  
Pham Quang Nam ◽  
Tran Quang Duc ◽  
Phan Van Tan
Keyword(s):  
Lead Time ◽  
Heavy Rainfall ◽  
Three Dimensional ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Data Types ◽  
Warm Start ◽  
Central Highlands ◽  
Atmospheric Motion Vectors ◽  
The Impact

This study investigates and assesses the impact of assimilating data types (observed data surface, sounding, and satellite-derived atmospheric motion vectors – AMVs) for the Weather Research and Forecasting (WRF) in forecasting heavy rainfall over Central Highlands region, due to the impact of hurricane Damrey. The WRF model combined with the Gridpoint Statistical Interpolation (GSI) system, was started running at 12Z 03/11/2017, and 84h forecasts in advance, with two kinds for running assimilation "cold start" and "warm start", and with the three-dimensional variational data assimilation (3D-Var) method. The results showed that assimilated cases have improved forecasting about spatial distribution and amount of rainfall at a 24-hour lead time, in which, the "warm start" for better forecasting. Notably, the assimilation of AMVs data with the "warm start" run has improved forecasting quality of heavy rainfall, the POD, FAR, and CSI indicators are the best at the 24-hour lead time, for rainfall thresholds greater than 80mm.    

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 An Observational and Modeling Study of the Processes Leading to Deep, Moist Convection in Complex Terrain

2014 ◽  
Vol 142 (8) ◽  
pp. 2687-2708 ◽  
Author(s):  
Tammy M. Weckwerth ◽  
Lindsay J. Bennett ◽  
L. Jay Miller ◽  
Joël Van Baelen ◽  
Paolo Di Girolamo ◽  
...  
Keyword(s):  
Water Vapor ◽  
Three Dimensional ◽  
Wrf Model ◽  
Convective Precipitation ◽  
Spatial And Temporal Variability ◽  
Moist Convection ◽  
Wind Fields ◽  
Vosges Mountains ◽  
Deep Moist Convection ◽  
Convection Initiation

Abstract A case study of orographic convection initiation (CI) that occurred along the eastern slopes of the Vosges Mountains in France on 6 August 2007 during the Convective and Orographically-Induced Precipitation Study (COPS) is presented. Global positioning system (GPS) receivers and two Doppler on Wheels (DOW) mobile radars sampled the preconvective and storm environments and were respectively used to retrieve three-dimensional tomographic water vapor and wind fields. These retrieved data were supplemented with temperature, moisture, and winds from radiosondes from a site in the eastern Rhine Valley. High-resolution numerical simulations with the Weather Research and Forecasting (WRF) Model were used to further investigate the physical processes leading to convective precipitation. This unique, time-varying combination of derived water vapor and winds from observations illustrated an increase in low-level moisture and convergence between upslope easterlies and downslope westerlies along the eastern slope of the Vosges Mountains. Uplift associated with these shallow, colliding boundary layer flows eventually led to the initiation of moist convection. WRF reproduced many features of the observed complicated flow, such as cyclonic (anticyclonic) flow around the southern (northern) end of the Vosges Mountains and the east-side convergent flow below the ridgeline. The WRF simulations also illustrated spatial and temporal variability in buoyancy and the removal of the lids prior to convective development. The timing and location of CI from the WRF simulations was surprisingly close to that observed.

scholarly journals Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction (EGMAP): project overview and status

2021 ◽  
Author(s):  
Florian Zus ◽  
Galina Dick ◽  
Jens Wickert
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Wrf Model ◽  
Severe Weather ◽  
Global Navigation Satellite Systems ◽  
Current Status ◽  
Total Delay ◽  
Numerical Weather ◽  
Data Products ◽  
The Impact

<p>Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing, becoming a common part of our everyday life.  A geophysical key application is atmospheric water vapor monitoring using GNSS ground station data. GNSS water vapor data, derived from regional ground networks hereby close gaps in the established meteorological observing systems. No other observing system provides data with such high temporal and spatial resolution. The data from European GNSS networks are therefore already widely operationally used to improve regional weather forecasts in several countries. However, the impact of the currently provided data products to the forecast systems is still limited due to the limited atmospheric information content, which is provided by the currently used Zenith Total Delay (ZTD) data.</p><p>In this talk we introduce the new project EGMAP (Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction). This project will pioneer the development and usage of next generation data products; tropospheric gradients. The new data products, developed and provided within the project, are expected to improve the impact of the currently provided GNSS data to weather forecast systems. The main innovations, which will be addressed by the project are: (1) Developments to provide high quality ZTDs and tropospheric gradients in near-real-time for the German SAPOS network; (2) Developments to make use of ZTDs and tropospheric gradients in numerical weather prediction, i.e., implement operators in the variational/ensemble data assimilation system of the Weather Research and Forecasting (WRF) model; (3) Impact studies with the state of the art numerical weather model. In this talk we provide an overview and the current status of the project.</p>

Forecast Evaluation of an Observing System Simulation Experiment Assimilating Both Radar and Satellite Data

2014 ◽  
Vol 142 (1) ◽  
pp. 107-124 ◽  
Author(s):  
Thomas A. Jones ◽  
Jason A. Otkin ◽  
David J. Stensrud ◽  
Kent Knopfmeier
Keyword(s):  
Water Vapor ◽  
Satellite Data ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water ◽  
Brightness Temperatures ◽  
Central United States ◽  
Correlation Statistics ◽  
The Impact

Abstract In the first part of this study, Jones et al. compared the relative skill of assimilating simulated radar reflectivity and radial velocity observations and satellite 6.95-μm brightness temperatures TB and found that both improved analyses of water vapor and cloud hydrometeor variables for a cool-season, high-impact weather event across the central United States. In this study, the authors examine the impact of the observations on 1–3-h forecasts and provide additional analysis of the relationship between simulated satellite and radar data observations to various water vapor and cloud hydrometeor variables. Correlation statistics showed that the radar and satellite observations are sensitive to different variables. Assimilating 6.95-μm TB primarily improved the atmospheric water vapor and frozen cloud hydrometeor variables such as ice and snow. Radar reflectivity proved more effective in both the lower and midtroposphere with the best results observed for rainwater, graupel, and snow. The impacts of assimilating both datasets decrease rapidly as a function of forecast time. By 1 h, the effects of satellite data become small on forecast cloud hydrometeor values, though it remains useful for atmospheric water vapor. The impacts of radar data last somewhat longer, sometimes up to 3 h, but also display a large decrease in effectiveness by 1 h. Generally, assimilating both satellite and radar data simultaneously generates the best analysis and forecast for most cloud hydrometeor variables.

Understanding the Dynamics of a Heavy Rainfall Event using Multivariate Ensemble Sensitivity Analysis 

2021 ◽  
Author(s):  
Babitha George ◽  
Govindan Kutty
Keyword(s):  
Sensitivity Analysis ◽  
Covariance Matrix ◽  
Heavy Rainfall ◽  
Initial Conditions ◽  
Wrf Model ◽  
Control Area ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Ensemble Forecasts ◽  
The Impact

<p>Ensemble forecasts have proven useful for investigating the dynamics in a wide variety of atmospheric systems and they might be useful for diagnosing the source of forecast uncertainty in multi-scale flows. Ensemble Sensitivity Analysis (ESA) uses ensemble forecasts to evaluate the impact of changes in initial conditions on subsequent forecasts. ESA leads to a simple univariate regression by approximating the analysis covariance matrix with the corresponding diagonal matrix. On the contrary, the multivariate ensemble sensitivity computes sensitivity based on a more general multivariate regression that retains the full covariance matrix. The purpose of this study is to examine the performance of multivariate ensemble sensitivity over univariate by applying it to a heavy rainfall event that happened over the Himalayan foothills in June 2013. The ensemble forecasts and analyses are generated using the Advanced Research version of the Weather Research and Forecasting (WRF) model DART based Ensemble Kalman Filter. Initial results are promising and the sensitivity shows similar patterns for both univariate and multivariate methods. The reflectivity forecast for both methods are characterized by lower temperatures and increased moisture in the control area at 850 hPa level. Compared to multivariate, univariate ensemble sensitivity overestimates the magnitude of sensitivity for temperature. But the sensitivity for the moisture is the same in both methods.</p>

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