Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

Abstract The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

Download Full-text

A scale-dependent blending scheme for WRFDA: impact on regional weather forecasting

Geoscientific Model Development ◽

10.5194/gmd-7-1819-2014 ◽

2014 ◽

Vol 7 (4) ◽

pp. 1819-1828 ◽

Cited By ~ 11

Author(s):

H. Wang ◽

X.-Y. Huang ◽

D. Xu ◽

J. Liu

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Weather Forecasting ◽

Domain Size ◽

Small Scale ◽

Forecast Errors ◽

Blending Method ◽

Environmental Prediction ◽

Regional Forecasts ◽

The Impact

Abstract. Due to limitation of the domain size and limited observations used in regional data assimilation and forecasting systems, regional forecasts suffer a general deficiency in effectively representing large-scale features such as those in global analyses and forecasts. In this paper, a scale-dependent blending scheme using a low-pass Raymond tangent implicit filter was implemented in the Data Assimilation system of the Weather Research and Forecasting model (WRFDA) to reintroduce large-scale weather features from global model analysis into the WRFDA analysis. The impact of the blending method on regional forecasts was assessed by conducting full cycle data assimilation and forecasting experiments for a 2-week-long period in September 2012. It is found that there are obvious large-scale forecast errors in the regional WRFDA system running in full cycle mode without the blending scheme. The scale-dependent blending scheme can efficiently reintroduce the large-scale information from National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses, and keep small-scale information from WRF analyses. The blending scheme is shown to reduce analysis and forecasting error of wind, temperature and humidity up to 24 h compared to the full cycle experiments without blending. It is also shown to increase precipitation prediction skills in the first 6 h forecasts.

Download Full-text

A scale-dependent blending scheme for WRFDA: impact on regional weather forecasting

Geoscientific Model Development Discussions ◽

10.5194/gmdd-7-2455-2014 ◽

2014 ◽

Vol 7 (2) ◽

pp. 2455-2482

Author(s):

H. Wang ◽

X.-Y. Huang ◽

D. Xu ◽

J. Liu

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Weather Forecasting ◽

Domain Size ◽

Small Scale ◽

Forecast Errors ◽

Blending Method ◽

Environmental Prediction ◽

Regional Forecasts ◽

The Impact

Abstract. Due to limitation of the domain size and limited observations used in regional data assimilation and forecasting systems, regional forecasts suffer a general deficiency in effectively representing large-scale features such as those in global analyses and forecasts. In this paper, a scale-dependent blending scheme using a low-pass Raymond tangent implicit filter was implemented in the Data Assimilation system of the Weather Research and Forecasting model (WRFDA) to re-introduce large-scale weather features from global model analysis into the WRFDA analysis. The impact of the blending method on regional forecasts was assessed by conducting full cycle data assimilation and forecasting experiments for a two-week long period in September 2012. It is found that there are obvious large-scale forecast errors in the regional WRFDA system running in full cycle mode without the blending scheme. The scale-dependent blending scheme can efficiently re-introduce the large-scale information from National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses, and keep small-scale information from WRF analyses. The blending scheme is shown to reduce analysis and forecasting error of wind, temperature and humidity up to 24 h compared to the full cycle experiments without blending. It is also shown to increase precipitation prediction skills in the first 6 h forecasts.

Download Full-text

Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

Remote Sensing ◽

10.3390/rs13112103 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2103

Author(s):

Yuchen Liu ◽

Jia Liu ◽

Chuanzhe Li ◽

Fuliang Yu ◽

Wei Wang

Keyword(s):

Data Assimilation ◽

Northern China ◽

Horizontal Resolution ◽

Radar Reflectivity ◽

Small Scale ◽

Time Interval ◽

Outer Domain ◽

Storm Prediction ◽

The Impact ◽

Assimilation Time

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

Download Full-text

Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

Monthly Weather Review ◽

10.1175/mwr-d-19-0045.1 ◽

2019 ◽

Vol 148 (1) ◽

pp. 63-81 ◽

Cited By ~ 10

Author(s):

Kevin Bachmann ◽

Christian Keil ◽

George C. Craig ◽

Martin Weissmann ◽

Christian A. Welzbacher

Keyword(s):

Data Assimilation ◽

Deep Convection ◽

Radar Data ◽

Ensemble Prediction ◽

Small Scale ◽

Model Errors ◽

Ensemble Prediction System ◽

Beneficial Impact ◽

The Impact ◽

Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

Download Full-text

Variational Assimilation of Cloud Liquid/Ice Water Path and Its Impact on NWP

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0243.1 ◽

2015 ◽

Vol 54 (8) ◽

pp. 1809-1825 ◽

Cited By ~ 18

Author(s):

Yaodeng Chen ◽

Hongli Wang ◽

Jinzhong Min ◽

Xiang-Yu Huang ◽

Patrick Minnis ◽

...

Keyword(s):

Data Assimilation ◽

Liquid Water ◽

Weather Prediction ◽

Wrf Model ◽

Liquid Water Path ◽

Cloud Liquid Water ◽

Water Path ◽

Ice Water Path ◽

Ice Water ◽

The Impact

AbstractAnalysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

Download Full-text

Fog forecasting for Schiphol airport at sub-kilometre scale.

10.5194/egusphere-egu21-155 ◽

2021 ◽

Author(s):

Gert-Jan Steeneveld ◽

Roosmarijn Knol

Keyword(s):

Spatial Resolution ◽

Numerical Models ◽

Wrf Model ◽

Horizontal Resolution ◽

Physical Processes ◽

Radiation Fog ◽

Fog Forecasting ◽

Elevation Data ◽

The Individual ◽

The Impact

Fog is a critical weather phenomenon for safety and operations in aviation. Unfortunately, the forecasting of radiation fog remains challenging due to the numerous physical processes that play a role and their complex interactions, in addition to the vertical and horizontal resolution of the numerical models. In this study we evaluate the performance of the Weather Research and Forecasting (WRF) model for a radiation fog event at Schiphol Amsterdam Airport (The Netherlands) and further develop the model towards a 100 m grid spacing. Hence we introduce high resolution land use and land elevation data. In addition we study the role of gravitational droplet settling, advection of TKE, top-down diffusion caused by strong radiative cooling at the fog top. Finally the impact of heat released by the terminal areas on the fog formation is studied. The model outcomes are evaluated against 1-min weather observations near multiple runways at the airport.Overall we find the WRF model shows an reasonable timing of the fog onset and is well able to reproduce the visibility and meteorological conditions as observed during the case study. The model appears to be relatively insensitive to the activation of the individual physical processes. An increased spatial resolution to 100 m generally results in a better timing of the fog onset differences up to three hours, though not for all runways. The effect of the refined landuse dominates over the effect of refined elevation data. The modelled fog dissipation systematically occurs 3-4 h hours too early, regardless of physical processes or spatial resolution. Finally, the introduction of heat from terminal buildings delays the fog onset with a maximum of two hours, an overestimated visibility of 100-200 m and a decrease of the LWC with 0.10-0.15 g/kg compared to the reference.

Download Full-text

Investigation of the impact of thermodynamic profiles of ground based lidar systems on short-range forecast skill by means of ETKF-hybrid 3DVAR data assimilation

10.5194/egusphere-egu21-9467 ◽

2021 ◽

Author(s):

Rohith Thundathil ◽

Thomas Schwitalla ◽

Andreas Behrendt ◽

Diego Lange ◽

Florian Späth ◽

...

Keyword(s):

Data Assimilation ◽

Wrf Model ◽

Absolute Humidity ◽

Raman Lidar ◽

High Definition ◽

Background Error ◽

Ground Based Lidar ◽

Ensemble Transform Kalman Filter ◽

Definition Of ◽

The Impact

Ground based active remote-sensing instruments have proved its potential through its high quality observations of thermodynamic profiles. In this study, thermodynamic profiles obtained from the temperature Raman lidar (TRL) and the water-vapour differential absorption lidar (DIAL) of the University of Hohenheim (UHOH) are assimilated into the Weather Research and Forecasting model data assimilation (WRFDA) system through a new forward operator for absolute humidity and mixing ratio developed in-house. Thermodynamic DA was performed either with the deterministic 3-dimensional variational (3DVAR) DA system or with the hybrid 3DVAR-Ensemble Transform Kalman Filter (ETKF) approach. We used data of the High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2 project Observation Prototype Experiment (HOPE). The WRF model was configured for a central European domain at a convection permitting resolution of 2.5 km spatial grid increment and 100 levels in the vertical with fine resolution in the planetary boundary layer (PBL). The assimilation experiments were conducted in a rapid update cycle (RUC) mode with an hourly update frequency. The hybrid 3DVAR-ETKF DA system was incorporated with an adaptive inflation scheme using a set of 10 ensemble members each with the same configuration as the previous experiments for the 3DVAR.&#160; We will present the results of three DA experiments. In the first experiment (CONV_DA), or the control run, only assimilation of the conventional observations was carried out with 3DVAR DA. The second experiment (QT_DA) was a 3DVAR DA assimilating WVMR and temperature together in addition to the conventional dataset. The third experiment (QT_HYB_DA) assimilated WVMR and temperature together in addition to the conventional dataset with Hybrid DA. The WVMR RMSE with respect to the WVDIAL reduced by 41 % in 3DVAR and still reduced to 51 % in QT_HYB_DA compared to CONV_DA. Although temperature RMSE with respect to TRL increased by 5 % in QT_DA, RMSE significantly reduced to 47 % in QT_HYB_DA compared to CONV_DA. The correlation between the temperature and WVMR variables in the background error covariance matrix of the 3DVAR, which is static and not flow-dependent, limited the improvement in temperature. Flow-dependency in Hybrid DA improved the error correlations. We also present results of a collaborative effort with the Raman lidar for meteorological observation (RALMO) from the MeteoSwiss and the Atmospheric Raman Temperature and Humidity Sounder (ARTHUS) using even finer model resolution. The initial results of the new study will also be presented here.

Download Full-text

A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment

The Cryosphere ◽

10.5194/tc-12-2287-2018 ◽

2018 ◽

Vol 12 (7) ◽

pp. 2287-2306 ◽

Cited By ~ 8

Author(s):

Gaia Piazzi ◽

Guillaume Thirel ◽

Lorenzo Campo ◽

Simone Gabellani

Keyword(s):

Data Assimilation ◽

Particle Filter ◽

Multivariate Data ◽

Model Parameters ◽

Parameter Perturbation ◽

Point Scale ◽

Meteorological Forcing ◽

System Sensitivity ◽

Filter Performance ◽

The Impact

Abstract. The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling – particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size.The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application.

Download Full-text

Reducing TC Position Uncertainty in an Ensemble Data Assimilation and Prediction System: A Case Study of Typhoon Fanapi (2010)

Weather and Forecasting ◽

10.1175/waf-d-17-0152.1 ◽

2018 ◽

Vol 33 (2) ◽

pp. 561-582 ◽

Cited By ~ 4

Author(s):

Kuan-Jen Lin ◽

Shu-Chih Yang ◽

Shuyi S. Chen

Keyword(s):

Data Assimilation ◽

Negative Impact ◽

Wrf Model ◽

Track Forecast ◽

Background Error ◽

Position Uncertainty ◽

Background Error Covariance ◽

Error Covariance ◽

The Impact

Abstract Ensemble-based data assimilation (EDA) has been used for tropical cyclone (TC) analysis and prediction with some success. However, the TC position spread determines the structure of the TC-related background error covariance and affects the performance of EDA. With an idealized experiment and a real TC case study, it is demonstrated that observations in the core region cannot be optimally assimilated when the TC position spread is large. To minimize the negative impact from large position uncertainty, a TC-centered EDA approach is implemented in the Weather Research and Forecasting (WRF) Model–local ensemble transform Kalman filter (WRF-LETKF) assimilation system. The impact of TC-centered EDA on TC analysis and prediction of Typhoon Fanapi (2010) is evaluated. Using WRF Model nested grids with 4-km grid spacing in the innermost domain, the focus is on EDA using dropsonde data from the Impact of Typhoons on the Ocean in the Pacific field campaign. The results show that the TC structure in the background mean state is improved and that unrealistically large ensemble spread can be alleviated. The characteristic horizontal scale in the background error covariance is smaller and narrower compared to those derived from the conventional EDA approach. Storm-scale corrections are improved using dropsonde data, which is more favorable for TC development. The analysis using the TC-centered EDA is in better agreement with independent observations. The improved analysis ameliorates model shock and improves the track forecast during the first 12 h and landfall at 72 h. The impact on intensity prediction is mixed with a better minimum sea level pressure and overestimated peak winds.

Download Full-text

Evaluating and Improving the Impact of the Atmospheric Stability and Orography on Surface Winds in the WRF Model

Monthly Weather Review ◽

10.1175/mwr-d-15-0449.1 ◽

2016 ◽

Vol 144 (7) ◽

pp. 2685-2693 ◽

Cited By ~ 17

Author(s):

Raquel Lorente-Plazas ◽

Pedro A. Jiménez ◽

Jimy Dudhia ◽

Juan P. Montávez

Keyword(s):

Atmospheric Stability ◽

Wrf Model ◽

Grid Cell ◽

Momentum Equation ◽

Small Scale ◽

Surface Winds ◽

Boundary Layer Height ◽

Stable Conditions ◽

Simulated Surface ◽

The Impact

Abstract This study assesses the impact of the atmospheric stability on the turbulent orographic form drag (TOFD) generated by unresolved small-scale orography (SSO) focusing on surface winds. With this aim, several experiments are conducted with the Weather Research and Forecasting (WRF) Model and they are evaluated over a large number of stations (318 at 2-m height) in the Iberian Peninsula with a year of data. In WRF, Jiménez and Dudhia resolved the SSO by including a factor in the momentum equation, which is a function of the orographic variability inside a grid cell. It is found that this scheme can improve the simulated surface winds, especially at night, but it can underestimate the winds during daytime. This suggests that TOFD can be dependent on the PBL’s stability. To inspect and overcome this limitation, the stability conditions are included in the SSO parameterization to maintain the intensity of the drag during stable conditions while attenuating it during unstable conditions. The numerical experiments demonstrate that the inclusion of stability effects on the SSO drag parameterization improves the simulated surface winds at diurnal, monthly, and annual scales by reducing the systematic daytime underestimation of the original scheme. The correction is especially beneficial when both the convective velocity and the boundary layer height are used to characterize the unstable conditions.

Download Full-text