Surface Data Assimilation and Near-Surface Weather Prediction over Complex Terrain

2016 ◽  
pp. 219-240 ◽  
Author(s):  
Zhaoxia Pu
Keyword(s):  
Data Assimilation ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Near Surface ◽  
Surface Data ◽  
Surface Weather

scholarly journals Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 587
Author(s):  
Magnus Lindskog ◽  
Tomas Landelius
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Numerical Weather ◽  
Extreme Precipitation Events ◽  
Surface Data ◽  
Enhanced Surface ◽  
Precipitation Events

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

scholarly journals Impact of surface data assimilation using an ensemble Kalman filter technique on the improvement of weather prediction

2019 ◽  
Vol 1127 ◽  
pp. 012041
Author(s):  
N J Trilaksono ◽  
M Taqiyya ◽  
N Dewani ◽  
I D G A Junnaedhi ◽  
E Riawan ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Filter Technique ◽  
Surface Data

scholarly journals Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

2016 ◽  
Author(s):  
N. S. Wagenbrenner ◽  
J. M. Forthofer ◽  
B. K. Lamb ◽  
K. S. Shannon ◽  
B. W. Butler
Keyword(s):  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Prediction Models ◽  
Wildland Fire ◽  
Weather Prediction ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Wind Model ◽  
Near Surface ◽  
Numerical Weather

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

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 Assimilation of MWHS-2/FY-3C 183 GHz Channels Using a Dynamic Emissivity Retrieval and Its Impacts on Precipitation Forecasts: A Southwest Vortex Case

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Keyi Chen ◽  
Jiao Fan ◽  
Zhipeng Xian
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Numerical Weather ◽  
Microwave Humidity Sounder ◽  
Southwest Vortex ◽  
Polar Orbiting Satellite ◽  
Better Than

The dynamic emissivity retrieved from window channels of the microwave humidity sounder II (MWHS-2) onboard the China Meteorological Administration’s FengYun (FY)-3C polar orbiting satellite can provide more realistic emissivity over lands and potentially improve the numerical weather prediction (NWP) forecasts. However, whether the assimilation with the dynamic emissivity works for the precipitation forecasts over the complex geography is less investigated. In this paper, a typical precipitating case generated by the Southwest Vortex is selected and the Weather Research and Forecasting data assimilation (WRFDA) system is applied to examine the impacts of assimilating MWHS-2/FY-3C with the uses of the emissivity atlas and the dynamic emissivity on the forecasts. The results indicate that the use of the dynamic emissivity retrieved from the 89 GHz channel of MWHS-2/FY-3C apparently increases the used data number for assimilation and does improve the initial fields and the 24-hour forecasts (from 0000 UTC 24 June 2016 to 0000 UTC 25 June 2016) of precipitation distribution and intensity except for the rainfall over 100 mm. But these positive impacts are not evidently better than those with the emissivity atlas. In general, these results still suggest that the future use of the dynamic emissivity in the assimilation over the complex terrain is promising.

scholarly journals A Numerical Sensitivity Study on the Impact of Soil Moisture on Convection-Related Parameters and Convective Precipitation over Complex Terrain

2011 ◽  
Vol 68 (12) ◽  
pp. 2971-2987 ◽  
Author(s):  
Christian Barthlott ◽  
Norbert Kalthoff
Keyword(s):  
Soil Moisture ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Sensitivity Study ◽  
Convective Precipitation ◽  
Small Scale ◽  
Near Surface ◽  
Initial Soil Moisture ◽  
Initial Soil ◽  
The Impact

Abstract The impact of soil moisture on convection-related parameters and convective precipitation over complex terrain is studied by numerical experiments using the nonhydrostatic Consortium for Small-Scale Modeling (COSMO) model. For 1 day of the Convective and Orographically Induced Precipitation Study (COPS) conducted during summer 2007 in southwestern Germany and eastern France, initial soil moisture is varied from −50% to +50% of the reference run in steps of 5%. As synoptic-scale forcing is weak on the day under investigation, the triggering of convection is mainly due to soil–atmosphere interactions and boundary layer processes. Whereas a systematic relationship to soil moisture exists for a number of variables (e.g., latent and sensible fluxes at the ground, near-surface temperature, and humidity), a systematic increase of 24-h accumulated precipitation with increasing initial soil moisture is only present in the simulations that are drier than the reference run. The time evolution of convective precipitation can be divided into two regimes with different conditions to initiate and foster convection. Furthermore, the impact of soil moisture is different for the initiation and modification of convective precipitation. The results demonstrate the high sensitivity of numerical weather prediction to initial soil moisture fields.

scholarly journals Assimilating Surface Weather Observations from Complex Terrain into a High-Resolution Numerical Weather Prediction Model

2007 ◽  
Vol 135 (3) ◽  
pp. 1037-1054 ◽  
Author(s):  
Xingxiu Deng ◽  
Roland Stull
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Final Analysis ◽  
Numerical Weather Prediction Model ◽  
Near Surface ◽  
Weather Observations ◽  
Surface Weather ◽  
Grid Points

Abstract An anisotropic surface analysis method based on the mother–daughter (MD) approach has been developed to spread valley station observations to grid points in circuitous steep valleys. In this paper, the MD approach is further refined to allow spreading the mountain-top observations to grid points near neighboring high ridges across valleys. Starting with a 3D first guess from a high-resolution mesoscale model forecast, surface weather observations are assimilated into the boundary layer, and pseudo-upper-air data (interpolated from the coarser-resolution analyses from major operational centers) are assimilated into the free atmosphere. Incremental analysis updating is then used to incorporate the final analysis increments (the difference between the final analysis and the first guess) into a high-resolution numerical weather prediction model. The MD approaches (including one with shoreline refinement) are compared with other objective analysis methods using case examples and daily mesoscale real-time forecast runs during November and December 2004. This study further confirms that the MD approaches outperform the other methods, and that the shoreline refinement achieves better analysis quality than the basic MD approach. The improvement of mountain-top refinement over the basic MD approach increases with the percentage of mountain-top stations, which is usually low. Higher skill in predicting near-surface potential temperature is found when surface information is spread upward throughout the boundary layer instead of at only the bottom model level. The results show improved near-surface forecasts of temperature and humidity that are directly assimilated into the model, but poorer forecasts of near-surface winds and precipitation, which are not assimilated into the model.

scholarly journals Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

2016 ◽  
Vol 16 (8) ◽  
pp. 5229-5241 ◽  
Author(s):  
Natalie S. Wagenbrenner ◽  
Jason M. Forthofer ◽  
Brian K. Lamb ◽  
Kyle S. Shannon ◽  
Bret W. Butler
Keyword(s):  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Prediction Models ◽  
Wildland Fire ◽  
Weather Prediction ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Wind Model ◽  
Near Surface ◽  
Numerical Weather

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

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