scholarly journals Using 3D-Var Data Assimilation for Improving the Accuracy of Initial Condition of Weather Research and Forecasting (WRF) Model in Java Region (Case Study : 23 January 2015)

2016 ◽  
Vol 30 (2) ◽  
pp. 112
Author(s):  
Novvria Sagita ◽  
Rini Hidayati ◽  
Rahmat Hidayat ◽  
Indra Gustari ◽  
Fatkhuroyan Fatkhuroyan
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Dew Point ◽  
Radiosonde Data ◽  
Weather Research And Forecasting ◽  
Observation Data ◽  
Initial Condition ◽  
Condition Model ◽  
Weather Research

Weather Research and Forecasting (WRF) is a numerical weather prediction model developed by various parties due to its open source, but the WRF has the disadvantage of low accuracy in weather prediction. One reason of low accuracy  of model is inaccuracy initial condition model to the actual atmospheric conditions. Techniques to improve the initial condition model is the observation data assimilation. In this study, we used three-dimensional variational (3D-Var) to perform data assimilation of some observation data. Observational data used in data assimilation are observation data from basic stations, non-basic stations, radiosonde data, and The Binary Universal Form for the Representation of meteorological data (BUFR) data from the National Centers for Environmental Prediction (NCEP) , and aggregate observation data from all stations. The aim of this study compares the effect of data assimilation with different data observation on January 23, 2015 at 00.00 UTC for Java island region. The results showed that changes root mean square error (RMSE) of surface temperature from 2° C to 1.7° C - 2.4° C, dew point from 2.1o C to 1.9o  C - 1.4o C, relative humidity from 16.1% to 3.5% - 14.5% after the data assimilation.

scholarly journals PENGARUH ASIMILASI DATA PENGINDERAAN JAUH (RADAR DAN SATELIT) PADA PREDIKSI CUACA NUMERIK UNTUK ESTIMASI CURAH HUJAN (IMPACT OF REMOTE SENSING DATA ASSIMILATION (RADAR AND SATELLITE) ON NUMERICAL WEATHER PREDICTION FOR RAINFALL ESTIMATION)

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Jaka A. I. Paski
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Research And Forecasting ◽  
Observation Data ◽  
Initial Condition ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Weather Research

One of the main problems in numerical weather modeling was the inaccuracy of initial condition data (initial conditions). This study reinforced the influence of assimilation of remote sensing observation data on initial conditions for predictive numerical rainfall in BMKG radar area Tangerang (Province of Banten and DKI Jakarta) on January 24, 2016. The procedure applied to rainfall forecast was the Weather Research and Forecasting model (WRF) with a down-to-down multi-nesting technique from Global Forecast System (GFS) output, the model was assimilated to radar and satellite image observation data using WRF Data Assimilation (WRFDA) 3DVAR system. Data was used as preliminary data from surface observation data, EEC C-Band radar data, AMSU-A satellite sensor data and MHS sensors. The analysis was done qualitatively by looking at the measurement scale. Observation data was used to know rainfall data. The results of the study showed that producing rainfall predictions with different assimilation of data produced different predictions. In general, there were improvements in the rainfall predictions with assimilation of satellite data was showing the best results. Abstrak Salah satu masalah utama pada pemodelan cuaca numerik adalah ketidak-akuratan data kondisi awal (initial condition). Penelitian ini menguji pengaruh asimilasi data observasi penginderaan jauh pada kondisi awal untuk prediksi numerik curah hujan di wilayah cakupan radar cuaca BMKG Tangerang (Provinsi Banten dan DKI Jakarta) pada 24 Januari 2016. Prosedur yang diterapkan pada prakiraan curah hujan adalah model Weather Research and Forecasting (WRF) dengan teknik multi-nesting yang di-downscale dari keluaran Global Forecast System (GFS), model ini diasimilasikan dengan data hasil observasi citra radar dan satelit menggunakan WRF Data Assimilation (WRFDA) sistem 3DVAR. Data yang digunakan sebagai kondisi awal berasal dari data observasi permukaan, data C-Band radar EEC, data satelit sensor AMSU-A dan sensor MHS. Analisis dilakukan secara kualitatif dengan melihat nilai prediksi spasial distribusi hujan terhadap data observasi GSMaP serta metode bias curah hujan antara model dan observasi digunakan untuk mengevaluasi pengaruh data asimilasi untuk prediksi curah hujan. Hasil penelitian yang diperoleh menunjukkan prediksi curah hujan dengan asimilasi data yang berbeda menghasilkan prediksi yang juga berbeda. Secara umum, asimilasi data penginderaan jauh memberikan perbaikan hasil prediksi estimasi curah hujan di mana asimilasi menggunakan data satelit menunjukan hasil yang paling baik.

Parallelization Strategies for the GPS Radio Occultation Data Assimilation with a Nonlocal Operator in the Weather Research and Forecasting Model

2014 ◽  
Vol 31 (9) ◽  
pp. 2008-2014 ◽  
Author(s):  
Xin Zhang ◽  
Ying-Hwa Kuo ◽  
Shu-Ya Chen ◽  
Xiang-Yu Huang ◽  
Ling-Feng Hsiao
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Observation Operator ◽  
Gps Radio Occultation ◽  
Phase Observation ◽  
Excess Phase ◽  
Weather Research

Abstract The nonlocal excess phase observation operator for assimilating the global positioning system (GPS) radio occultation (RO) sounding data has been proven by some research papers to produce significantly better analyses for numerical weather prediction (NWP) compared to the local refractivity observation operator. However, the high computational cost and the difficulties in parallelization associated with the nonlocal GPS RO operator deter its application in research and operational NWP practices. In this article, two strategies are designed and implemented in the data assimilation system for the Weather Research and Forecasting Model to demonstrate the capability of parallel assimilation of GPS RO profiles with the nonlocal excess phase observation operator. In particular, to solve the parallel load imbalance problem due to the uneven geographic distribution of the GPS RO observations, round-robin scheduling is adopted to distribute GPS RO observations among the processing cores to balance the workload. The wall clock time required to complete a five-iteration minimization on a demonstration Antarctic case with 106 GPS RO observations is reduced from more than 3.5 h with a single processing core to 2.5 min with 106 processing cores. These strategies present the possibility of application of the nonlocal GPS RO excess phase observation operator in operational data assimilation systems with a cutoff time limit.

scholarly journals Optimization of the WRFV3.7 adjoint model

2018 ◽  
Author(s):  
Qiang Cheng ◽  
Juanjuan Liu ◽  
Bin Wang
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Time Cost ◽  
Input Output ◽  
Adjoint Model ◽  
New Strategy ◽  
Weather Research

Abstract. This work focused on a new strategy for productively improving the performance of adjoint models. By using several techniques including the push/pop-free method, careful Input/Output (IO) analysis and the use of the conception of adjoint locality, we reduced the adjoint cost of the Weather Research and Forecasting plus (WRFPLUS) by almost half on different numbers of processors especially with a slight decrease in total memory. Several experiments are conducted using the four-dimensional variational data assimilation (4DVar) method. The results show that the total time cost of running a 4DVar application is decreased by approximately 1/3.

scholarly journals Error Correction of Meteorological Data Obtained with Mini-AWSs Based on Machine Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Ji-Hun Ha ◽  
Yong-Hyuk Kim ◽  
Hyo-Hyuc Im ◽  
Na-Young Kim ◽  
Sangjin Sim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Atmospheric Pressure ◽  
Observation Interval ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Observation Data ◽  
Pressure Data ◽  
Weather Forecasts ◽  
Weather Events

Severe weather events occur more frequently due to climate change; therefore, accurate weather forecasts are necessary, in addition to the development of numerical weather prediction (NWP) of the past several decades. A method to improve the accuracy of weather forecasts based on NWP is the collection of more meteorological data by reducing the observation interval. However, in many areas, it is economically and locally difficult to collect observation data by installing automatic weather stations (AWSs). We developed a Mini-AWS, much smaller than AWSs, to complement the shortcomings of AWSs. The installation and maintenance costs of Mini-AWSs are lower than those of AWSs; Mini-AWSs have fewer spatial constraints with respect to the installation than AWSs. However, it is necessary to correct the data collected with Mini-AWSs because they might be affected by the external environment depending on the installation area. In this paper, we propose a novel error correction of atmospheric pressure data observed with a Mini-AWS based on machine learning. Using the proposed method, we obtained corrected atmospheric pressure data, reaching the standard of the World Meteorological Organization (WMO; ±0.1 hPa), and confirmed the potential of corrected atmospheric pressure data as an auxiliary resource for AWSs.

scholarly journals Characterization of downwelling radiance measured from a ground-based microwave radiometer using numerical weather prediction model data

2016 ◽  
Vol 9 (1) ◽  
pp. 281-293 ◽  
Author(s):  
M.-H. Ahn ◽  
H. Y. Won ◽  
D. Han ◽  
Y.-H. Kim ◽  
J.-C. Ha
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Reference Data ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Radiosonde Data ◽  
Absolute Calibration ◽  
Observation Data ◽  
Numerical Weather ◽  
Better Than

Abstract. The ground-based microwave sounding radiometers installed at nine weather stations of Korea Meteorological Administration alongside with the wind profilers have been operating for more than 4 years. Here we apply a process to assess the characteristics of the observation data by comparing the measured brightness temperature (Tb) with reference data. For the current study, the reference data are prepared by the radiative transfer simulation with the temperature and humidity profiles from the numerical weather prediction model instead of the conventional radiosonde data. Based on the 3 years of data, from 2010 to 2012, we were able to characterize the effects of the absolute calibration on the quality of the measured Tb. We also showed that when clouds are present the comparison with the model has a high variability due to presence of cloud liquid water therefore making cloudy data not suitable for assessment of the radiometer's performance. Finally we showed that differences between modeled and measured brightness temperatures are unlikely due to a shift in the selection of the center frequency but more likely due to spectroscopy issues in the wings of the 60 GHz absorption band. With a proper consideration of data affected by these two effects, it is shown that there is an excellent agreement between the measured and simulated Tb. The regression coefficients are better than 0.97 along with the bias value of better than 1.0 K except for the 52.28 GHz channel which shows a rather large bias and variability of −2.6 and 1.8 K, respectively.

scholarly journals Microphysics of summer clouds in central West Antarctica simulated by the Polar Weather Research and Forecasting Model (WRF) and the Antarctic Mesoscale Prediction System (AMPS)

2019 ◽  
Vol 19 (19) ◽  
pp. 12431-12454 ◽  
Author(s):  
Keith M. Hines ◽  
David H. Bromwich ◽  
Sheng-Hung Wang ◽  
Israel Silber ◽  
Johannes Verlinde ◽  
...  
Keyword(s):  
Liquid Water ◽  
Radiative Forcing ◽  
Weather Prediction ◽  
The Arctic ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Prediction System ◽  
West Antarctic ◽  
The Antarctic ◽  
Weather Research

Abstract. The Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) provided a highly detailed set of remote-sensing and surface observations to study Antarctic clouds and surface energy balance, which have received much less attention than for the Arctic due to greater logistical challenges. Limited prior Antarctic cloud observations have slowed the progress of numerical weather prediction in this region. The AWARE observations from the West Antarctic Ice Sheet (WAIS) Divide during December 2015 and January 2016 are used to evaluate the operational forecasts of the Antarctic Mesoscale Prediction System (AMPS) and new simulations with the Polar Weather Research and Forecasting Model (WRF) 3.9.1. The Polar WRF 3.9.1 simulations are conducted with the WRF single-moment 5-class microphysics (WSM5C) used by the AMPS and with newer generation microphysics schemes. The AMPS simulates few liquid clouds during summer at the WAIS Divide, which is inconsistent with observations of frequent low-level liquid clouds. Polar WRF 3.9.1 simulations show that this result is a consequence of WSM5C. More advanced microphysics schemes simulate more cloud liquid water and produce stronger cloud radiative forcing, resulting in downward longwave and shortwave radiation at the surface more in agreement with observations. Similarly, increased cloud fraction is simulated with the more advanced microphysics schemes. All of the simulations, however, produce smaller net cloud fractions than observed. Ice water paths vary less between the simulations than liquid water paths. The colder and drier atmosphere driven by the Global Forecast System (GFS) initial and boundary conditions for AMPS forecasts produces lesser cloud amounts than the Polar WRF 3.9.1 simulations driven by ERA-Interim.

scholarly journals GPS Radio Occultation Data Assimilation in the AREM Regional Numerical Weather Prediction Model for Flood Forecasts

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Wei Cheng ◽  
Youping Xu ◽  
Zhiwu Deng ◽  
Chunli Gu
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Radiosonde Data ◽  
Light Rain ◽  
Gps Radio Occultation ◽  
Study Results ◽  
Occultation Data ◽  
Radio Occultation Data

Based on the Backward Four-Dimensional Variational Data Assimilation (Backward-4DVar) system with the Advanced Regional Eta-coordinate Model (AREM), which is capable of assimilating radio occultation data, a heavy rainfall case study is performed using GPS radio occultation (GPS RO) data and routine GTS data on July 5, 2007. The case study results indicate that the use of radio occultation data after quality control can improve the quality of the analysis to be similar to that of the observations and, thus, have a positive effect when improving 24-hour rainfall forecasts. Batch tests for 119 days from May to August during the flood season in 2009 show that only the use of GPS RO data can make positive improvements in both 24-hour and 48-hour regional rainfall forecasts and obtain a better B score for 24-hour forecasts and better TS score for 48-hour forecasts. When using radio occultation refractivity data and conventional radiosonde data, the results indicate that radio occultation refractivity data can achieve a better performance for 48-hour forecasts of light rain and heavy rain.

scholarly journals Numerical Weather Prediction for Himalayan Complex Terrain: Prospects of Variational Data Assimilation

2016 ◽  
Vol 3 (1) ◽  
pp. 67
Author(s):  
Sangeeta Maharjan ◽  
Ram P. Regmi
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Environmental Research ◽  
Data Sets ◽  
Observation Data ◽  
Ongoing Research ◽  
Medium Range Weather Forecast ◽  
Research Activities ◽  
Wrf Modeling

<p>As part of the ongoing research activities at National Atmospheric Resource and Environmental Research Laboratory (NARERL) to realize high spatial and temporal resolution weather forecasts for Nepal, the Weather Research and Forecasting (WRF) modeling system performance with the National Center for Environmental Protection (NCEP) and National Center for Medium Range Weather Forecast (NCMRWF) initialization global meteorological data sets and the effect of surface observation data assimilation have been examined. The study shows that WRF modeling system reasonably well predicts the diurnal variation of upcoming weather events with both the data sets. The observation data assimilation from entire weather station distributed over the country may lead to the significant improvement in the accuracy and reliability of extended period of forecast. However, upper air observation data assimilation would be necessary to achieve desired precision and reliability of extended weather forecast.</p><p>Journal of Nepal Physical Society Vol.3(1) 2015: 67-72</p>

scholarly journals Regional Climate–Weather Research and Forecasting Model

2012 ◽  
Vol 93 (9) ◽  
pp. 1363-1387 ◽  
Author(s):  
Xin-Zhong Liang ◽  
Min Xu ◽  
Xing Yuan ◽  
Tiejun Ling ◽  
Hyun I. Choi ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Regional Climate ◽  
Weather Forecast ◽  
Ecosystem Model ◽  
Climate Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Physical Processes ◽  
Terrestrial Hydrology ◽  
Weather Research

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model. This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

scholarly journals Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 489 ◽  
Author(s):  
Jürgen Helmert ◽  
Aynur Şensoy Şorman ◽  
Rodolfo Alvarado Montero ◽  
Carlo De Michele ◽  
Patricia de Rosnay ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Climate Models ◽  
Weather Prediction ◽  
Data Availability ◽  
Observation Data ◽  
Climate Change Scenarios ◽  
Numerical Weather ◽  
The Future

The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

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