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.

Assimilation of GPS Radio Occultation Data for an Intense Atmospheric River with the NCEP Regional GSI System

2011 ◽  
Vol 139 (7) ◽  
pp. 2170-2183 ◽  
Author(s):  
Zaizhong Ma ◽  
Ying-Hwa Kuo ◽  
F. Martin Ralph ◽  
Paul J. Neiman ◽  
Gary A. Wick ◽  
...  
Keyword(s):  
Western Europe ◽  
Radio Occultation ◽  
Single Case ◽  
Horizontal Structure ◽  
Observation Operator ◽  
Gps Radio Occultation ◽  
Phase Observation ◽  
Excess Phase ◽  
The Impact

Abstract This paper uses a case study to explore the potential of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Challenging Minisatellite Payload (CHAMP) global positioning system (GPS) radio occultation (RO) satellite data over the eastern Pacific Ocean to improve analyses and mesoscale forecasts of landfalling atmospheric rivers (ARs) along the U.S. West Coast. The case study is from early November 2006 and was a very high-impact event in the Pacific Northwest where it created torrential rainfall and severe flooding. Recent studies have shown that the COSMIC data offshore have the ability to better define the vertical and horizontal structure of the strong AR. This paper extends the earlier work by assessing the impact of assimilating the COSMIC data into the Advanced Research Weather Research and Forecasting (ARW-WRF) mesoscale numerical model (using a nested mode with 36-, 12-, and 4-km grid sizes) on a key 24-h forecast. The data are assimilated using NCEP’s Gridpoint Statistical Interpolation (GSI), and impacts are evaluated using Special Sensor Microwave Imager (SSM/I) satellite observations over the ocean and precipitation observations over land. The assimilation of GPS RO soundings made use of a local refractivity observation operator as well as an advanced nonlocal excess phase observation operator that considers the effects of atmospheric horizontal gradients. The results show that the assimilation of GPS RO soundings improved the moisture analysis for this AR event. This result supports conclusions from earlier observing systems simulation experiment (OSSE) studies, but in a real event. The use of a nonlocal excess phase observation operator can produce larger and more robust analysis increments. Although this is a single case study, the results are likely representative of the potential impacts of assimilating COSMIC data in other extreme AR and precipitation events and in other regions affected by landfalling ARs, for example, western Europe, western South America, and New Zealand.

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 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 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.

PRACTICAL ASPECTS OF APPLICATION OF METEOROLOGICAL WEATHER PREDICTION MODELS FOR THE NEED OF AGRICULTURAL SECTOR OF ECONOMY

2017 ◽  
Author(s):  
Mykhailo Frolov ◽  
Keyword(s):  
Negative Impact ◽  
Weather Prediction ◽  
Mesoscale Modeling ◽  
Extreme Weather Events ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Medium Term ◽  
Short Term ◽  
The Impact ◽  
Weather Research

Different aspects of the application of short-term and long-term meteorological forecasting in the work of agricultural companies of different scales were investigated in this paper. Systematic assessment and consideration of weather factors in the work of agricultural companies of various scales is very important on the way to effective management and increase profits, especially in today's climate change. The use of existing global meteorological models does not always allow to obtain a detailed and qualitative forecast for individual areas, which is important for assessing the impact of weather on crops. The advantages of using the mesoscale Weather Research and Forecasting Model (WRF) for agricultural companies are considered. The results of short-term and medium- term WRF modeling can be used by agricultural campaigns to assess the potential negative impact of weather conditions on crops, as well as in preventive decisions to reduce the negative impact of extreme weather events on crops. The problem of the need for significant computing resources for mesoscale modeling can be solved by using commercial Cloud platforms, which are more cost-effective than purchasing high-performance computer systems (HPC). The scheme of application of short-term and medium-term weather forecasting in agricultural companies by realization of mesoscale modeling with Weather Research and Forecasting Model (WRF) and with application of cloud technologies was developed. The result of the proposed scheme are graphic products of meteorological quantities and weather maps of the modeling area (where crops are located), which is a ready product for analysis by agricultural companies for further agricultural and technical decision making process.

scholarly journals A Decade of Antarctic Science Support Through Amps

2012 ◽  
Vol 93 (11) ◽  
pp. 1699-1712 ◽  
Author(s):  
Jordan G. Powers ◽  
Kevin W. Manning ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Arthur M. Cayette
Keyword(s):  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Prediction System ◽  
Numerical Weather ◽  
The Antarctic ◽  
The U.S ◽  
Weather Research

The Antarctic Mesoscale Prediction System (AMPS) is a real-time numerical weather prediction (NWP) system covering Antarctica that has served a remarkable range of groups and activities for a decade. It employs the Weather Research and Forecasting model (WRF) on varying-resolution grids to generate numerical guidance in a variety of tailored products. While its priority mission has been to support the forecasters of the U.S. Antarctic Program, AMPS has evolved to assist a host of scientific and logistical needs for an international user base. The AMPS effort has advanced polar NWP and Antarctic science and looks to continue this into another decade. To inform those with Antarctic scientific and logistical interests and needs, the history, applications, and capabilities of AMPS are discussed.

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.

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