Improvement of numerical weather prediction of heavy rain event using radar data assimilation using rapid update cycle method in Jabodetabek Region

Quantitative Precipitation Forecasting (QPF) is among the most central challenges of atmospheric prediction systems. The primary aim of such a task is the generation of accurate estimates of heavy precipitation events associated with severe weather, atmospheric fronts and heavy convective rainfalls. QPF is still among the most intricate challenges of Numerical Weather Prediction. The efforts in this direction are mainly concentrated on improving model formulations for microphysics and convective process and remote sensing data assimilation.This paper describes the first results with the regional radar signal processing chain that provides the radar data assimilation (RDA) in the Harmonie convection permitting numerical model. This task is performed for a case study focusing on a wintertime frontal cyclone over the island of Cyprus. Reflectivity measurements from two weather radars, at Larnaka and Paphos, are exploited for simulations of severe weather conditions associated with this synoptic-scale system. Through the variational assimilation procedure, the model takes into account the atmospheric processes occurring in the upstream flow which can be outside the area of radar measurements. The focus is on the precipitable water vapor content and its changes during the cyclone evolution, as well as on the impact of the radar data assimilation on precipitation estimates.The results show that the numerical experiments exhibit, in general, a suitable simulation of precipitable water at different stages of the cyclone. In particular, the bulk of the rainfall volume exhibits three stages: intensive rain on the cyclone's frontal zone, weaker precipitation immediately behind the front, and the secondary enhancement of rainfall. The largest corrections due to RDA are of up to 5 mm and occur during the approach of the cyclone frontal zone in a form of enhanced rainfall over the whole area, but more prominently in weak precipitation locations.

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Improving Numerical Weather Prediction of Rainfall Events Using Radar Data Assimilation

Indonesian Journal of Geography ◽

10.22146/ijg.44924 ◽

2019 ◽

Vol 51 (3) ◽

pp. 273 ◽

Cited By ~ 1

Author(s):

Miranti Indri Hastuti ◽

Jaka Anugrah Ivanda Paski ◽

Fatkhuroyan Fatkhuroyan

Keyword(s):

Numerical Model ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Weather Radar ◽

Radar Data ◽

Observation Data ◽

Numerical Weather ◽

Rain Events ◽

Radar Data Assimilation

Data assimilation is one of method to improve initial atmospheric conditions data in numerical weather prediction. The assimilation of weather radar data that has quite extensive and tight data is considered to be able to improve the quality of weather prediction and analysis. This study aims to investigate the effect of assimilation of Doppler weather radar data in Weather Research Forecasting (WRF) numerical model for the prediction of heavy rain events in the Jabodetabek area with dates representing four seasons respectively on 20 February 2017, 3 April 2017, 13 June 2017, and 9 November 2017. For this purpose, the reflectivity (Z) and radial velocity (V) data from Plan Position Indicator (PPI) product and reflectivity (Z) data from Constant Altitude PPI (CAPPI) product were assimilated using WRFDA (WRF Data Assimilation) numerical model with 3DVar (The Three Dimensional Variational) system. The output of radar data assimilation and without assimilation of the numerical model of WRF is verified by spatial with GSMaP data and by point with precipitation observation data. In general, WRF radar assimilation provides a better simulation of spatial and point rain events compared to the WRF model without assimilation which is improvements of rain prediction from WRF radar data assimilation would be more visible in areas close to radar sources and not echo-blocked from fixed objects, and more visible during the rainy season

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Using Radar Wind Observations to Improve Mesoscale Numerical Weather Prediction

Weather and Forecasting ◽

10.1175/waf936.1 ◽

2006 ◽

Vol 21 (4) ◽

pp. 502-522 ◽

Cited By ~ 27

Author(s):

Qingyun Zhao ◽

John Cook ◽

Qin Xu ◽

Paul R. Harasti

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Three Dimensional ◽

Radar Data ◽

The Impact ◽

Data Assimilation System ◽

Radar Data Assimilation ◽

Assimilation System

Abstract A high-resolution radar data assimilation system is presented for high-resolution numerical weather prediction models. The system is under development at the Naval Research Laboratory for the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System. A variational approach is used to retrieve three-dimensional dynamical fields of atmospheric conditions from multiple-Doppler radar observations of radial velocity within a limited area. The methodology is described along with a preliminary evaluation of the impact of assimilated radar data on model forecasts using a case study of a squall line that occurred along the east coast of the United States on 9 May 2003. Results from the experiments show a significant impact from the assimilated radar radial velocity data on the model forecast of not just dynamical but also hydrological fields at all model levels for the duration of the storm. A verification system has also been developed to assess the radar data assimilation impact, and the results show improvements in the three-dimensional wind forecasts but relatively small changes in the prediction of storm locations. This study highlights the need to develop a continuous radar data assimilation system to maximize the impact of the data.

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On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

Monthly Weather Review ◽

10.1175/mwr-d-17-0028.1 ◽

2018 ◽

Vol 146 (2) ◽

pp. 599-622 ◽

Cited By ~ 7

Author(s):

David D. Flagg ◽

James D. Doyle ◽

Teddy R. Holt ◽

Daniel P. Tyndall ◽

Clark M. Amerault ◽

...

Keyword(s):

Boundary Layer ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Research Laboratory ◽

Weather Prediction ◽

Ocean Model ◽

Naval Research ◽

Unmanned Aerial System ◽

Numerical Weather ◽

The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

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Background Error in WRF Model

WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT ◽

10.37394/232015.2020.16.63 ◽

2020 ◽

Vol 16 ◽

Keyword(s):

Data Assimilation ◽

Physical Properties ◽

Covariance Matrix ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Wrf Model ◽

Background Error ◽

Numerical Weather ◽

Error Covariance ◽

First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

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Spin-up time from switching the microphysics scheme in the assimilation cycle and impacts on the precipitation forecast quality

10.5194/ems2021-379 ◽

2021 ◽

Author(s):

Sven Ulbrich ◽

Christian Welzbacher ◽

Kobra Khosravianghadikolaei ◽

Michael Hoff ◽

Alberto de Lozar ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Lead Time ◽

Weather Prediction ◽

Radar Data ◽

Precipitation Forecast ◽

Additional Measure ◽

Microphysics Scheme ◽

Numerical Weather ◽

Complementary Approach ◽

Forecast Quality

The SINFONY project at Deutscher Wetterdienst (DWD) aims to produce seamless precipitation forecast products from minutes up to 12 hours, with particular focus on convective events. While the near future predictions are typically from nowcasting procedures using radar data, the numerical weather prediction (NWP) aims at longer time scales. The lead-time in the latest available forecast is usually too long for merging both the nowcasting and NWP output to produce reliable seamless predictions.At DWD, the current forecasts are produced by the short range numerical weather prediction (SRNWP) making use of a continuous assimilation cycle with relatively long cutoff times and using 1-moment microphysics. In order to reduce the differences in the precipitation to the nowcasting on the NWP side, we use two different approaches. First, we reduce the lead-time from the model start by running 1-hourly forecasts based on an assimilation cycle with shorter data cutoff. Secondly, we use new observational systems in the assimilation cycle, such as radar or satellite data to capture and represent strong convective activity. This procedure is called Rapid Update Cycle (RUC). As an additional measure, we introduce a 2-Moment microphysics scheme into the numerical model, resulting in a better representation of the radar reflectivities. In order to keep the model state similar to that of the SRNWP, the RUC is a time limited assimilation cycle starting from forecasts of the SRNWP at pre-defined times.The introduction of the 2-Moment scheme leads to a spin-up affecting both the assimilation cycle and the short forecasts. The resulting effects are analysed by comparison with the corresponding assimilation cycle using the 1-Moment scheme. As a complementary approach for the analysis, the routine cycle is run with the 2-Moment scheme. The forecast quality is used as a measure to compare the results with respect to precipitation and additional observed parameters. It is shown in how far the resulting improvements are related to the assimilation and momentum scheme, or to the higher frequency of forecasts.

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The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation

Meteorology and Atmospheric Physics ◽

10.1007/s00703-001-0595-6 ◽

2003 ◽

Vol 82 (1-4) ◽

pp. 139-170 ◽

Cited By ~ 298

Author(s):

Ming Xue ◽

Donghai Wang ◽

Jidong Gao ◽

Keith Brewster ◽

Kelvin K. Droegemeier

Keyword(s):

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Prediction System ◽

Advanced Regional Prediction System ◽

Numerical Weather ◽

Regional Prediction

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Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction

Monthly Weather Review ◽

10.1175/mwr-d-17-0344.1 ◽

2019 ◽

Vol 147 (4) ◽

pp. 1107-1126 ◽

Cited By ~ 9

Author(s):

Jonathan Poterjoy ◽

Louis Wicker ◽

Mark Buehner

Keyword(s):

Data Assimilation ◽

Particle Filter ◽

Numerical Weather Prediction ◽

Large Scale ◽

Sequential Monte Carlo ◽

Prediction Models ◽

Weather Prediction ◽

Coupled Systems ◽

State Variables ◽

Numerical Weather

Abstract A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by particle filters, which make no parametric assumptions for the underlying prior and posterior error distributions. The filter also treats the underlying dynamical system as a set of loosely coupled systems to effectively localize the effect observations have on posterior state estimates. This property greatly reduces the number of particles—or ensemble members—required for its implementation. For these reasons, the method is called the local particle filter. The current manuscript summarizes algorithmic advances made to the local particle filter following recent tests performed over a hierarchy of dynamical systems. The revised filter uses modified vector weight calculations and probability mapping techniques from earlier studies, and new strategies for improving filter stability in situations where state variables are observed infrequently with very accurate measurements. Numerical experiments performed on low-dimensional data assimilation problems provide evidence that supports the theoretical benefits of the new improvements. As a proof of concept, the revised particle filter is also tested on a high-dimensional application from a real-time weather forecasting system at the NOAA/National Severe Storms Laboratory (NSSL). The proposed changes have large implications for researchers applying the local particle filter for real applications, such as data assimilation in numerical weather prediction models.

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