The analysis and impact of simulated high-resolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter

2011 ◽  
Vol 112 (1-2) ◽  
pp. 41-61 ◽  
Author(s):  
Jili Dong ◽  
Ming Xue ◽  
Kelvin Droegemeier
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Ensemble Kalman Filter ◽  
Radar Data ◽  
Convective Storms ◽  
Surface Observations

scholarly journals Analysis of a Tornadic Mesoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band and WSR-88D Radar Data

2011 ◽  
Vol 139 (11) ◽  
pp. 3446-3468 ◽  
Author(s):  
Nathan Snook ◽  
Ming Xue ◽  
Youngsun Jung
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Radar Data ◽  
Convective System ◽  
Foundation Engineering ◽  
X Band ◽  
Regional Prediction ◽  
Mesoscale Convective

Abstract One of the goals of the National Science Foundation Engineering Research Center (ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) is to improve storm-scale numerical weather prediction (NWP) by collecting data with a dense X-band radar network that provides high-resolution low-level coverage, and by assimilating such data into NWP models. During the first spring storm season after the deployment of four radars in the CASA Integrated Project-1 (IP-1) network in southwest Oklahoma, a tornadic mesoscale convective system (MCS) was captured by CASA and surrounding Weather Surveillance Radars-1988 Doppler (WSR-88Ds) on 8–9 May 2007. The MCS moved across northwest Texas and western and central Oklahoma; two tornadoes rated as category 1 on the enhanced Fujita scale (EF-1) and one tornado of EF-0 intensity were reported during the event, just to the north of the IP-1 network. This was the first tornadic convective system observed by CASA. To quantify the impacts of CASA radar data in storm-scale NWP, a set of data assimilation experiments were performed using the Advanced Regional Prediction System (ARPS) ensemble Kalman filter (EnKF) system configured with full model physics and high-resolution terrain. Data from four CASA IP-1 radars and five WSR-88Ds were assimilated in some of the experiments. The ensemble contained 40 members, and radar data were assimilated every 5 min for 1 h. While the assimilation of WSR-88D data alone was able to produce a reasonably accurate analysis of the convective system, assimilating CASA data in addition to WSR-88D data is found to improve the representation of storm-scale circulations, particularly in the lowest few kilometers of the atmosphere, as evidenced by analyses of gust front position and comparison of simulated Vr with observations. Assimilating CASA data decreased RMS innovation of the resulting ensemble mean analyses of Z, particularly in early assimilation cycles, suggesting that the addition of CASA data allowed the EnKF system to more quickly achieve a good result. Use of multiple microphysics schemes in the forecast ensemble was found to alleviate underdispersion by increasing the ensemble spread. This work is the first assimilating real CASA data into an NWP model using EnKF.

Developing High-resolution Air Quality Reanalysis Dataset over China for Years 2013-2018 Based on Ensemble Kalman Filter and Surface Observations from CNEMC

2020 ◽  
Author(s):  
Lei Kong ◽  
Xiao Tang ◽  
Jiang Zhu ◽  
Zifa Wang ◽  
Huangjian Wu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Air Quality ◽  
High Resolution ◽  
Ensemble Kalman Filter ◽  
Air Pollutants ◽  
Reanalysis Data ◽  
Training Data ◽  
Reanalysis Dataset ◽  
Air Quality Prediction ◽  
Surface Observations

<p>A six-year long high-resolution Chinese air quality reanalysis datasets (CAQRA) covering the period 2013-2018 has been developed in this study by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) based on the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). This reanalysis provides the surface fields of six conventional air pollutants in China, namely PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO and O<sub>3</sub>, at high spatial (15km×15km) and temporal (1 hour) resolutions. This paper aims to document this dataset by providing the detailed descriptions of the assimilation system and presenting the first validation results for the reanalysis fields of air pollutants in China. A twenty-fold cross validation (CV) method was used to assess the quality of CAQRA. The CV results show that the CAQRA has excellent performances in reproducing the magnitude and variability of the air pollutants in China with the biases (normalized mean bias) of the reanalysis data about -2.6 (-4.9%) μg/m<sup>3</sup> for PM<sub>2.5</sub>, -6.8 (-7.6%) μg/m<sup>3</sup> for PM<sub>10</sub>, -2.0 (-8.5%) μg/m<sup>3</sup> for SO<sub>2</sub>, -2.3 (-6.9%) μg/m<sup>3</sup> for NO<sub>2</sub>, -0.06 (-6.1%) mg/m<sup>3</sup> for CO and -2.3 (-4.0%) μg/m<sup>3</sup> for O<sub>3</sub>. The interannual changes of the air quality in China were also well represented by the CAQRA in terms of the six air pollutants. Comparisons with previous datasets of daily PM<sub>2.5</sub>, SO<sub>2</sub> and NO<sub>2</sub> concentrations indicate that the CAQRA is more accurate with smaller RMSE values. We also compared our reanalysis dataset to the CAMSRA (The Copernicus Atmosphere Monitoring Service reanalysis) produced by ECMWF (European Centre for Medium-Range Weather Forecasts), which suggests that the CAQRA has higher accuracy in representing the surface air pollutants in China due to the assimilation of surface observations. This reanalysis dataset can provide us comprehensive pictures of the air quality in China from 2013 to 2018 with a complete spatial and temporal coverage, which can be used in the assessment of health impacts of air pollution, validation of model simulations and providing training data for the statistical or AI (Artificial Intelligence) based forecast.</p>

scholarly journals Radar Data Assimilation in the Canadian High-Resolution Ensemble Kalman Filter System: Performance and Verification with Real Summer Cases

2014 ◽  
Vol 142 (6) ◽  
pp. 2118-2138 ◽  
Author(s):  
Weiguang Chang ◽  
Kao-Shen Chung ◽  
Luc Fillion ◽  
Seung-Jong Baek
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Radar Data ◽  
Radial Velocities ◽  
Short Term ◽  
Radar Observations ◽  
Filter Convergence ◽  
Radar Data Assimilation

Abstract An 80-member high-resolution ensemble Kalman filter (HREnKF) is implemented for assimilating radar observations with the Canadian Meteorological Center’s (CMC’s) Global Environmental Multiscale Limited-Area Model (GEM-LAM). This system covers the Montréal, Canada, region and assimilates radar data from the McGill Radar Observatory with 4-km data thinning. The GEM-LAM operates in fully nonhydrostatic mode with 58 hybrid vertical levels and 1-km horizontal grid spacing. As a first step toward full radar data assimilation, only radial velocities are directly assimilated in this study. The HREnKF is applied on three 2011 summer cases having different precipitation structures: squall-line structure, isolated small-scale structures, and widespread stratiform precipitation. The short-term (<2 h) accuracy of the HREnKF analyses and forecasts is examined. In HREnKF, the ensemble spread is sufficient to cover the estimated error from innovations and lead to filter convergence. It results in part from a realistic initiation of HREnKF data assimilation cycle by using a Canadian regional EnKF system (itself coupled to a global EnKF) working at meso- and synoptic scales. The filter convergence is confirmed by the HREnKF background fields gradually approaching to radar observations as the assimilation cycling proceeds. At each analysis step, it is clearly shown that unobserved variables are significantly modified through HREnKF cross correlation of errors from the ensemble. Radar reflectivity observations are used to verify the improvements in analyses and short-term forecasts achievable by assimilating only radial velocities. Further developments of the analysis system are discussed.

scholarly journals Assimilation of Hourly Surface Observations with the Canadian High-Resolution Ensemble Kalman Filter

2017 ◽  
Vol 55 (4-5) ◽  
pp. 247-263
Author(s):  
Weiguang Chang ◽  
Dominik Jacques ◽  
Luc Fillion ◽  
Seung-Jong Baek
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Ensemble Kalman Filter ◽  
Surface Observations

scholarly journals Assimilation of High-Resolution Tropical Cyclone Observations with an Ensemble Kalman Filter Using HEDAS: Evaluation of 2008–11 HWRF Forecasts

2015 ◽  
Vol 143 (2) ◽  
pp. 511-523 ◽  
Author(s):  
Sim D. Aberson ◽  
Altuğ Aksoy ◽  
Kathryn J. Sellwood ◽  
Tomislava Vukicevic ◽  
Xuejin Zhang
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Tropical Cyclone ◽  
Ensemble Kalman Filter ◽  
Doppler Radar ◽  
Radar Data ◽  
Doppler Radar Data ◽  
Flight Level ◽  
Airborne Doppler Radar ◽  
Aircraft Data

Abstract NOAA has been gathering high-resolution, flight-level dropwindsonde and airborne Doppler radar data in tropical cyclones for almost three decades; the U.S. Air Force routinely obtained the same type and quality of data, excepting Doppler radar, for most of that time. The data have been used for operational diagnosis and for research, and, starting in 2013, have been assimilated into operational regional tropical cyclone models. This study is an effort to quantify the impact of assimilating these data into a version of the operational Hurricane Weather Research and Forecasting model using an ensemble Kalman filter. A total of 83 cases during 2008–11 were investigated. The aircraft whose data were used in the study all provide high-density flight-level wind and thermodynamic observations as well as surface wind speed data. Forecasts initialized with these data assimilated are compared to those using the model standard initialization. Since only NOAA aircraft provide airborne Doppler radar data, these data are also tested to see their impact above the standard aircraft data. The aircraft data alone are shown to provide some statistically significant improvement to track and intensity forecasts during the critical watch and warning period before projected landfall (through 60 h), with the Doppler radar data providing some further improvement. This study shows the potential for improved forecasts with regular tropical cyclone aircraft reconnaissance and the assimilation of data obtained from them, especially airborne Doppler radar data, into the numerical guidance.

scholarly journals Data Assimilation of the High-Resolution Sea Surface Temperature Obtained from the Aqua-Terra Satellites (MODIS-SST) Using an Ensemble Kalman Filter

2013 ◽  
Vol 5 (6) ◽  
pp. 3123-3139 ◽  
Author(s):  
Yasumasa Miyazawa ◽  
Hiroshi Murakami ◽  
Toru Miyama ◽  
Sergey Varlamov ◽  
Xinyu Guo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ensemble Kalman Filter ◽  
Sea Surface

Evaluation of Surface Analyses and Forecasts with a Multiscale Ensemble Kalman Filter in Regions of Complex Terrain

2011 ◽  
Vol 139 (6) ◽  
pp. 2008-2024 ◽  
Author(s):  
Brian C. Ancell ◽  
Clifford F. Mass ◽  
Gregory J. Hakim
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Complex Terrain ◽  
Error Variance ◽  
Small Scale ◽  
Observation Error ◽  
Grid Spacing ◽  
Background Error

Abstract Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

2014 ◽  
Vol 142 (1) ◽  
pp. 141-162 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan Snook ◽  
Guifu Zhang
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Cold Pool ◽  
Convective System ◽  
Polarimetric Radar ◽  
Microphysics Scheme ◽  
Mesoscale Convective ◽  
Double Moment

Abstract Doppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation.

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