Weather Forecasts by the WRF-ARW Model with the GSI Data Assimilation System in the Complex Terrain Areas of Southwest Asia

2009 ◽  
Vol 24 (4) ◽  
pp. 987-1008 ◽  
Author(s):  
J. Xu ◽  
S. Rugg ◽  
L. Byerle ◽  
Z. Liu
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Diurnal Cycle ◽  
Complex Terrain ◽  
Initial Conditions ◽  
Forecast Errors ◽  
Model Errors ◽  
Southwest Asia ◽  
Model Forecast ◽  
Near Surface

Abstract This paper will first describe the forecasting errors encountered from running the National Center for Atmospheric Research (NCAR) mesoscale model (the Advanced Research Weather Research and Forecasting model; ARW) in the complex terrain of southwest Asia from 1 to 31 May 2006. The subsequent statistical evaluation is designed to assess the model’s surface and upper-air forecast accuracy. Results show that the model biases caused by inadequate parameterization of physical processes are relatively small, except for the 2-m temperature, as compared to the nonsystematic errors resulting in part from the uncertainty in the initial conditions. The total model forecast errors at the surface show a substantial spatial heterogeneity; the errors are relatively larger in higher mountain areas. The performance of 2-m temperature forecasts is different from the other surface variables’ forecasts; the model forecast errors in 2-m temperature forecasts are closely related to the terrain configuration. The diurnal cycle variation of these near-surface temperature forecasts from the model is much smaller than what is observed. Second, in order to understand the role of the initial conditions in relation to the accuracy of the model forecasts, this study assimilated a form of satellite radiance data into this model through the Joint Center for Satellite Data Assimilation (JCSDA) analysis system called the Gridpoint Statistical Interpolation (GSI). The results indicate that on average over a 30-day experiment for the 24- and 48-h (second 24 h) forecasts, the satellite data provide beneficial information for improving the initial conditions and the model errors are reduced to some degree over some of the study locations. The diurnal cycle for some forecasting variables can be improved after satellite data assimilation; however, the improvement is very limited.

scholarly journals Remotely sensed latent heat fluxes for improving model predictions of soil moisture: a case study

2010 ◽  
Vol 7 (4) ◽  
pp. 6179-6205
Author(s):  
J. M. Schuurmans ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Latent Heat ◽  
Satellite Data ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Heat Fluxes ◽  
Model Errors ◽  
Small Catchment

Abstract. This paper investigates whether the use of remotely sensed latent heat fluxes improves the accuracy of spatially-distributed soil moisture predictions by a hydrological model. By using real data we aim to show the potential and limitations in practice. We use (i) satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL), provides us the spatial distribution of daily ETact and (ii) an operational physically based distributed (25 m×25 m) hydrological model of a small catchment (70 km2) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Firstly, model outcomes of ETact are compared to the processed satellite data. Secondly, we perform data assimilation that updates the modelled soil moisture. We show that remotely sensed ETact is useful in hydrological modelling for two reasons. Firstly, in the procedure of model calibration: comparison of modeled and remotely sensed ETact together with the outcomes of our data assimilation procedure points out potential model errors (both conceptual and flux-related). Secondly, assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area with forest and deep groundwater levels. As both ASTER and MODIS images were available for the same days, this study provides also an excellent opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides much better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

Examination of Errors in Near-Surface Temperature and Wind from WRF Numerical Simulations in Regions of Complex Terrain

2013 ◽  
Vol 28 (3) ◽  
pp. 893-914 ◽  
Author(s):  
Hailing Zhang ◽  
Zhaoxia Pu ◽  
Xuebo Zhang
Keyword(s):  
Complex Terrain ◽  
Forecast Error ◽  
Wrf Model ◽  
Atmospheric Temperature ◽  
Lower Atmosphere ◽  
Forecast Errors ◽  
Atmospheric Conditions ◽  
Near Surface ◽  
System A ◽  
Synoptic Forcing

Abstract The performance of an advanced research version of the Weather Research and Forecasting Model (WRF) in predicting near-surface atmospheric temperature and wind conditions under various terrain and weather regimes is examined. Verification of 2-m temperature and 10-m wind speed and direction against surface Mesonet observations is conducted. Three individual events under strong synoptic forcings (i.e., a frontal system, a low-level jet, and a persistent inversion) are first evaluated. It is found that the WRF model is able to reproduce these weather phenomena reasonably well. Forecasts of near-surface variables in flat terrain generally agree well with observations, but errors also occur, depending on the predictability of the lower-atmospheric boundary layer. In complex terrain, forecasts not only suffer from the model's inability to reproduce accurate atmospheric conditions in the lower atmosphere but also struggle with representative issues due to mismatches between the model and the actual terrain. In addition, surface forecasts at finer resolutions do not always outperform those at coarser resolutions. Increasing the vertical resolution may not help predict the near-surface variables, although it does improve the forecasts of the structure of mesoscale weather phenomena. A statistical analysis is also performed for 120 forecasts during a 1-month period to further investigate forecast error characteristics in complex terrain. Results illustrate that forecast errors in near-surface variables depend strongly on the diurnal variation in surface conditions, especially when synoptic forcing is weak. Under strong synoptic forcing, the diurnal patterns in the errors break down, while the flow-dependent errors are clearly shown.

Prediction of Tornado-Like Vortex (TLV) Embedded in the 8 May 2003 Oklahoma City Tornadic Supercell Initialized from the Subkilometer Grid Spacing Analysis Produced by the Dual-Resolution GSI-Based EnVar Data Assimilation System

2020 ◽  
Vol 148 (7) ◽  
pp. 2909-2934
Author(s):  
Yongming Wang ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Potential Temperature ◽  
Intensity Variation ◽  
Oklahoma City ◽  
Grid Spacing ◽  
Near Surface ◽  
Low Level ◽  
Cold Pools ◽  
The Impact

Abstract Explicit forecasts of a tornado-like vortex (TLV) require subkilometer grid spacing because of their small size. Most previous TLV prediction studies started from interpolated kilometer grid spacing initial conditions (ICs) rather than subkilometer grid spacing ICs. The tornadoes embedded in the 8 May 2003 Oklahoma City tornadic supercell are used to understand the impact of IC resolution on TLV predictions. Two ICs at 500-m and 2-km grid spacings are, respectively, produced through an efficient dual-resolution (DR) and a single-coarse-resolution (SCR) EnVar ingesting a 2-km ensemble. Both experiments launch 1-h forecasts at 500-m grid spacing. Diagnostics of data assimilation (DA) cycling reveal DR produces stronger and broader rear-flank cold pools, more intense downdrafts and updrafts with finer scales, and more hydrometeors at high altitudes through accumulated differences between two DA algorithms. Relative differences in DR, compared to SCR, include the integration from higher-resolution analyses, the update for higher-resolution backgrounds, and the propagation of ensemble perturbations along higher-resolution model trajectory. Predictions for storm morphology and cold pools are more realistic in DR than in SCR. The DR-TLV tracks match better with the observed tornado tracks than SCR-TLV in timing of intensity variation, and in duration. Additional experiments suggest 1) the analyzed kinematic variables strongly influence timing of intensity variation through affecting both low-level rear-flank outflow and midlevel updraft; 2) potential temperature analysis by DR extends the second track’s duration consistent with enhanced low-level stretching, delayed broadening large-scale downdraft, and (or) increased near-surface baroclinic vorticity supply; and 3) hydrometeor analyses have little impact on TLV predictions.

Evolutionary Algorithm-Based Error Parameterization Methods for Data Assimilation

2011 ◽  
Vol 139 (8) ◽  
pp. 2668-2685 ◽  
Author(s):  
Yulong Bai ◽  
Xin Li
Keyword(s):  
Genetic Algorithms ◽  
Data Assimilation ◽  
Evolutionary Algorithm ◽  
Initial Conditions ◽  
Fitness Function ◽  
Observation Interval ◽  
Substantial Effect ◽  
Model Errors ◽  
Lorenz Model ◽  
Error Adjustment

AbstractThe methods of parameterizing model errors have a substantial effect on the accuracy of ensemble data assimilation. After a review of the current error-handling methods, a new blending error parameterization method was designed to combine the advantages of multiplicative inflation and additive inflation. Motivated by evolutionary algorithm concepts that have been developed in the control engineering field for years, the authors propose a new data assimilation method coupled with crossover principles of genetic algorithms based on ensemble transform Kalman filters (ETKFs). The numerical experiments were developed based on the classic nonlinear model (i.e., the Lorenz model). Convex crossover, affine crossover, direction-based crossover, and blending crossover data assimilation systems were consequently designed. When focusing on convex crossover and affine crossover data assimilation problems, the error adjustment factors were investigated with respect to four aspects, which were the initial conditions of the Lorenz model, the number of ensembles, observation covariance, and the observation interval. A new data assimilation system, coupled with genetic algorithms, is proposed to solve the difficult problem of the error adjustment factor search, which is usually performed using trial-and-error methods. The results show that all of the methods can adaptively obtain the best error factors within the constraints of the fitness function.

scholarly journals Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

2021 ◽  
Vol 25 (1) ◽  
pp. 17-40
Author(s):  
Hylke E. Beck ◽  
Ming Pan ◽  
Diego G. Miralles ◽  
Rolf H. Reichle ◽  
Wouter A. Dorigo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Data ◽  
Irrigation Scheduling ◽  
Open Loop ◽  
Spatiotemporal Variability ◽  
Near Surface ◽  
Satellite Retrievals ◽  
Performance Ranking

Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

scholarly journals Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

2020 ◽  
Author(s):  
Hylke E. Beck ◽  
Ming Pan ◽  
Diego G. Miralles ◽  
Rolf H. Reichle ◽  
Wouter A. Dorigo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Data ◽  
Irrigation Scheduling ◽  
Open Loop ◽  
Spatiotemporal Variability ◽  
Near Surface ◽  
Satellite Retrievals ◽  
Performance Ranking

Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as open-loop models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo and six estimates from the HBV model with three precipitation inputs (ERA5, IMERG, and MSWEP) and with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5-cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. The median R ± interquartile range across all sites and products in each category was 0.66 ± 0.30 for the satellite products, 0.69 ± 0.25 for the open-loop models, and 0.72 ± 0.22 for the models with satellite data assimilation. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E, SMOS, AMSR2, and ASCAT, with the L-band-based SMAPL3E (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI), MeMo performed better on average (median R of 0.72 versus 0.67), mainly due to the inclusion of SMAPL3E. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

scholarly journals Influence of a portable near-surface observing network on experimental ensemble forecasts of deep convection hazards during VORTEX-SE

2021 ◽  
Author(s):  
Aaron J. Hill ◽  
Christopher C. Weiss ◽  
David C. Dowell
Keyword(s):  
Data Assimilation ◽  
Subjective Evaluation ◽  
Deep Convection ◽  
Local Analysis ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Southeast United States ◽  
Near Surface ◽  
Flow Through ◽  
The Impact

AbstractEnsemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification and Origins of Rotation in Tornadoes EXperiment – Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the southeast United States. StickNet observations are assimilated with an experimental version of the HighResolution RapidRefresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis.

scholarly journals A Comparison of the Hybrid and EnSRF Analysis Schemes in the Presence of Model Errors due to Unresolved Scales

2009 ◽  
Vol 137 (10) ◽  
pp. 3219-3232 ◽  
Author(s):  
Xuguang Wang ◽  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Craig H. Bishop
Keyword(s):  
Model Comparison ◽  
Additive Noise ◽  
Initial Conditions ◽  
Random Noise ◽  
Model Error ◽  
Forecast Errors ◽  
Model Errors ◽  
Hybrid Analysis ◽  
Error Covariance ◽  
Analysis Scheme

Abstract A hybrid analysis scheme is compared with an ensemble square root filter (EnSRF) analysis scheme in the presence of model errors as a follow-up to a previous perfect-model comparison. In the hybrid scheme, the ensemble perturbations are updated by the ensemble transform Kalman filter (ETKF) and the ensemble mean is updated with a hybrid ensemble and static background-error covariance. The experiments were conducted with a two-layer primitive equation model. The true state was a T127 simulation. Data assimilation experiments were conducted at T31 resolution (3168 complex spectral coefficients), assimilating imperfect observations drawn from the T127 nature run. By design, the magnitude of the truncation error was large, which provided a test on the ability of both schemes to deal with model error. Additive noise was used to parameterize model errors in the background ensemble for both schemes. In the first set of experiments, additive noise was drawn from a large inventory of historical forecast errors; in the second set of experiments, additive noise was drawn from a large inventory of differences between forecasts and analyses. The static covariance was computed correspondingly from the two inventories. The hybrid analysis was statistically significantly more accurate than the EnSRF analysis. The improvement of the hybrid over the EnSRF was smaller when differences of forecasts and analyses were used to form the random noise and the static covariance. The EnSRF analysis was more sensitive to the size of the ensemble than the hybrid. A series of tests was conducted to understand why the EnSRF performed worse than the hybrid. It was shown that the inferior performance of the EnSRF was likely due to the sampling error in the estimation of the model-error covariance in the mean update and the less-balanced EnSRF initial conditions resulting from the extra localizations used in the EnSRF.

scholarly journals An Advanced Data Assimilation System for the Chesapeake Bay: Performance Evaluation

2012 ◽  
Vol 29 (10) ◽  
pp. 1542-1557 ◽  
Author(s):  
Matthew J. Hoffman ◽  
Takemasa Miyoshi ◽  
Thomas W. N. Haine ◽  
Kayo Ide ◽  
Christopher W. Brown ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Chesapeake Bay ◽  
Nonlinear Dynamical Systems ◽  
Initial Conditions ◽  
Real Data ◽  
Ocean Modeling ◽  
Forecast Errors ◽  
Regional Ocean Modeling System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract An advanced data assimilation system, the local ensemble transform Kalman filter (LETKF), has been interfaced with a Regional Ocean Modeling System (ROMS) implementation on the Chesapeake Bay (ChesROMS) as a first step toward a reanalysis and improved forecast system for the Chesapeake Bay. The LETKF is among the most advanced data assimilation methods and is very effective for large, nonlinear dynamical systems with sparse data coverage. Errors in the Chesapeake Bay system are due more to errors in forcing than errors in initial conditions. To account for forcing errors, a forcing ensemble is used to drive the ensemble states for the year 2003. In the observing system simulation experiments (OSSEs) using the ChesROMS-LETKF system presented here, the filter converges quickly and greatly reduces the analysis and subsequent forecast errors in the temperature, salinity, and current fields in the presence of errors in wind forcing. Most of the improvement in temperature and currents comes from satellite sea surface temperature (SST), while in situ salinity profiles provide improvement to salinity. Corrections permeate through all vertical levels and some correction to stratification is seen in the analysis. In the upper Bay where the nature-run summer stratification is −0.2 salinity units per meter, stratification is improved from −0.01 per meter in the unassimilated model to −0.16 per meter in the assimilation. Improvements are seen in other parts of the Bay as well. The results from the OSSEs are promising for assimilating real data in the future.

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