scholarly journals Impacts of Assimilating ATMS Radiances on Heavy Rainfall Forecast in RMAPS-ST

2020 ◽  
Vol 12 (7) ◽  
pp. 1147
Author(s):  
Yanhui Xie ◽  
Min Chen ◽  
Jiancheng Shi ◽  
Shuiyong Fan ◽  
Jing He ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
Advanced Technology ◽  
Added Value ◽  
Rainfall Forecast ◽  
Hourly Rainfall ◽  
Radiance Data ◽  
The Impact

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work.

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

2014 ◽  
Vol 21 (5) ◽  
pp. 1027-1041 ◽  
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.

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

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.

scholarly journals Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 74
Author(s):  
Yajie Qi ◽  
Shuiyong Fan ◽  
Bai Li ◽  
Jiajia Mao ◽  
Dawei Lin
Keyword(s):  
Control Experiment ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Radar Data ◽  
Specific Humidity ◽  
Rainfall Forecast ◽  
Hourly Rainfall ◽  
Microwave Radiometers ◽  
The Impact ◽  
Precipitation Enhancement

Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing.

scholarly journals Impact of radar data assimilation using WRF three-dimensional variational system, for the simulation of a heavy rainfall case in Central Italy

2013 ◽  
Vol 6 (4) ◽  
pp. 7315-7353
Author(s):  
I. Maiello ◽  
R. Ferretti ◽  
S. Gentile ◽  
M. Montopoli ◽  
E. Picciotti ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Heavy Rainfall ◽  
Initial Conditions ◽  
Positive Impact ◽  
Central Italy ◽  
Three Dimensional ◽  
Warm Start ◽  
The Impact ◽  
Dwr Data

Abstract. This work is a first assessment of the role of Doppler Weather radar (DWR) data in a mesoscale model for the prediction of a heavy rainfall. The study analyzes the event occurred during 19–22 May 2008 in the urban area of Rome. The impact of the radar reflectivity and radial velocity acquired from Monte Midia Doppler radar, on the assimilation into the Weather Research Forecasting (WRF) model version 3.2, is discussed. The goal is to improve the WRF high resolution initial condition by assimilating DWR data and using ECMWF analyses as First Guess thus improving the forecast of surface rainfall. Several experiments are performed using different set of Initial Conditions (ECMWF analyses and warm start or cycling) and a different assimilation strategy (3 h-data assimilation cycle). In addition, 3DVAR (three-dimensional variational) sensitivity tests to outer loops are performed for each of the previous experiment to include the non-linearity in the observation operators. In order to identify the best ICs, statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a positive impact on the prediction of the heavy rainfall of this event, both assimilating reflectivity and radial velocity, together with conventional observations. Finally, warm start results in more accurate experiments as well as the outer loops strategy.

scholarly journals An Assessment of Satellite Radiance Data Assimilation in RMAPS

2018 ◽  
Vol 11 (1) ◽  
pp. 54 ◽  
Author(s):  
Yanhui Xie ◽  
Shuiyong Fan ◽  
Min Chen ◽  
Jiancheng Shi ◽  
Jiqin Zhong ◽  
...  
Keyword(s):  
North China ◽  
Initial Temperature ◽  
Initial Conditions ◽  
Positive Impact ◽  
Skill Score ◽  
Limited Area ◽  
Accumulated Rainfall ◽  
Satellite Radiance Data ◽  
Radiance Data ◽  
The Impact

Due to the availability of observations and the effectiveness of bias correction, it is still a challenge to assimilate data from the polar orbit satellites into a limited-area and frequently updated model. This study assessed the initial application of satellite radiance data from multiple platforms in the Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS). Satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) were used. Two 12-day retrospective runs were conducted to evaluate the impact of assimilating satellite radiance data on 0–24 h forecasts using RMAPS. The forecasts, initialized from analyses with and without satellite radiance data, were verified against observations. The results showed that satellite radiance data from AMSU-A and MHS had a positive impact on the initial conditions and the forecasts of RMAPS, even over the relatively data-rich area of North China. Compared to the control run that only assimilated conventional observations, an improvement of about 36.8% can be obtained for the temperature bias between 300 hPa and 850 hPa and 0.65% for the average RMSE. Satellite radiance observations from 1200 UTC contribute relatively significantly (77.8%) to the bias improvement of the initial temperature field. For the wind at 10 m, the bias and root-mean-square error (RMSE) both had a reduction for the 0–12 h forecast range. An improvement can be also found for the skill score of the 3-h accumulated rainfall below 10.0 mm in the first 12 h of the forecast range. There was a slight improvement in the skill score of the 6-h accumulated rainfall above 50 mm over North China, with a 20.7% improvement for the first 12 h of the forecast. The inclusion of satellite radiance observations was found to be beneficial for the initial temperature, which consequently improved the forecast skill of the 0–12 h range in the RMAPS.

Impact of Assimilating Wind Profiling Radar Observations on Convection-Permitting Quantitative Precipitation Forecasts during SCMREX

2016 ◽  
Vol 31 (4) ◽  
pp. 1271-1292 ◽  
Author(s):  
Xubin Zhang ◽  
Yali Luo ◽  
Qilin Wan ◽  
Weiyu Ding ◽  
Jiaxiang Sun
Keyword(s):  
Heavy Rainfall ◽  
Initial Conditions ◽  
Positive Impact ◽  
Southern China ◽  
Surface Wind ◽  
Precipitation Forecast ◽  
Horizontal Wind ◽  
False Alarms ◽  
The Impact ◽  
Partial Cycle

Abstract To improve the prediction of heavy rainfall in southern China during the prerainy season, horizontal wind data from wind profiling radars (WPRs) were assimilated in the partial-cycle data assimilation (DA) system based on a three-dimensional variational method. The analyses from the DA system are used as initial conditions for the convection-permitting Global/Regional Assimilation and Prediction System (GRAPES) model. The impact of assimilating WPR data on the quantitative precipitation forecast (QPF) in southern China was evaluated over the period of the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014, by comparing the results of a control experiment with WPR data assimilated and a denial experiment without WPR data. The positive impact of WPR DA was significant on the forecasts of atmospheric variables in the vertical and diagnostic fields at the surface, especially those of surface wind fields in the 0–6-h range. The inclusion of WPR data also improved the QPF skill of light and heavy rainfall throughout the 12-h forecast period by reducing the predicted spurious precipitation (thereby alleviating overestimations and false alarms), with the largest improvement in 6-h heavy rainfall forecasts. WPR DA considerably alleviated the spinup problem, remarkably improving the QPF of heavy rainfall (especially extreme rainfall). The improved representation of wind and moisture at lower levels in the analyses due to WPR DA was the physical cause of the QPF improvement, as is illustrated using a case study.

scholarly journals The Impact of Low Latency Satellite Sounder Observations on Local Severe Storm Forecasts in Regional NWP

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 650 ◽  
Author(s):  
Pei Wang ◽  
Jun Li ◽  
Timothy J. Schmit
Keyword(s):  
Positive Impact ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Advanced Technology ◽  
Low Latency ◽  
Telecommunication System ◽  
Global Telecommunication System ◽  
Direct Broadcast ◽  
The Impact ◽  
Data Latency

The forecasts of local severe storms (LSS) are highly dependent on how well the pre-convection environment is characterized in the numerical weather prediction (NWP) model analysis. The usefulness of the forecasts is highly dependent on how frequently the forecast is updated. Therefore, the data latency is critical for assimilation into regional NWP models for it to be able to assimilate more data within the data cut-off window. These low latency data can be obtained through direct broadcast sites and direct receiving systems. Observing system experiments (OSE) were performed to study the impact of data latency on the LSS forecasts. The experiments assimilated all existing observations including conventional data (from the global telecommunication system, GTS) and satellite sounder radiance data (AMSU-A (The Advanced Microwave Sounding Unit-A), ATMS (Advanced Technology Microwave Sounder), CrIS (Cross-track Infrared Sounder), and IASI (Infrared Atmospheric Sounding Interferometer)). They were carried out in a nested domain with a horizontal resolution of 9 km and 3 km in the weather research and forecasting (WRF) model. The forecast quality scores of the LSS precipitation forecasts were calculated and compared with different data cut-off widows to evaluate the impact of data latency. The results showed that low latency can lead to an improved and positive impact on precipitation and other forecasts, which indicates the potential application of LEO direct broadcast (DB) data in a high-resolution regional NWP for LSS forecasts.

Improving numerical simulation of typhoon LEKIMA(2019) through assimilating ATMS radiance data

2020 ◽  
Author(s):  
Lei Zhang ◽  
Baode Chen
Keyword(s):  
Numerical Simulation ◽  
Data Assimilation ◽  
Inner Core ◽  
Weather Prediction ◽  
Advanced Technology ◽  
Limited Area ◽  
Numerical Weather ◽  
Satellite Radiance Data ◽  
Radiance Data ◽  
The Impact

<p>Lacking of high-resolution observations over oceans is one of the major problems for the numerical simulation of the tropical cyclones (TC), especially for the tropical cyclone inner-core structure’s simulation. Satellite observations plays an important role in improving the forecast skills of numerical weather prediction (NWP) systems. Many studies have suggested that the assimilation of satellite radiance data can substantially improve the numerical weather forecast skills for global model. However, the performance of satellite radiance data assimilation in limited-area modeling systems is still controversial.</p><p>This study attempts to investigate the impact of assimilation of the Advanced Technology Microwave Sounder (ATMS) satellite radiances data and its role to improve the model initial condition and forecast of typhoon LEKIMA(2019) using a regional mesoscale model. In this study, detailed analysis of the data impact will be presented, also the results from different data assimilation methods and different data usage schemes will be discussed.</p>

Role of initial condition and parametric uncertainty in a severe hailstorm forecast

2021 ◽  
Author(s):  
Patrick Kuntze ◽  
Annette Miltenberger ◽  
Corinna Hoose ◽  
Michael Kunz
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Parametric Uncertainty ◽  
Radar Data ◽  
Cloud Microphysics ◽  
Initial Condition ◽  
Relative Contribution ◽  
Cloud Properties ◽  
Set Up ◽  
The Impact

<p>Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so potentially do uncertainties arising from the representation of physical processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties for the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.<br>To investigate the joint impact of initial condition and parametric uncertainty a large ensemble including perturbed initial conditions and systematic variations in several cloud microphysical parameters is conducted with the ICON model (at 1 km grid-spacing). The comparison of the baseline, unperturbed simulation to satellite, radiosonde, and radar data shows that the model reproduces the key features of the storm and its evolution. In particular also substantial hail precipitation at the surface is predicted. Here, we will present first results including the simulation set-up, the evaluation of the baseline simulation, and the variability of hail forecasts from the ensemble simulation.<br>In a later stage of the project we aim to assess the relative contribution of the introduced model variations to changes in the microphysical evolution of the storm and to the fore- cast uncertainty in larger-scale meteorological conditions.</p>

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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