scholarly journals Sensitivity of simulated temperature, precipitation, and global radiation to different WRF configurations over the Carpathian Basin for regional climate applications

2020 ◽  
Vol 55 (9-10) ◽  
pp. 2849-2866
Author(s):  
Ákos János Varga ◽  
Hajnalka Breuer
Keyword(s):  
Large Scale ◽  
Regional Climate ◽  
Global Radiation ◽  
Wrf Model ◽  
Carpathian Basin ◽  
Shortwave Radiation ◽  
Cold Bias ◽  
Parameterization Schemes ◽  
Downward Shortwave Radiation ◽  
Basin Region

Abstract In this study, the Weather Research and Forecasting (WRF) model is used to produce short-term regional climate simulations with several configurations for the Carpathian Basin region. The goal is to evaluate the performance of the model and analyze its sensitivity to different physical and dynamical settings, and input data. Fifteen experiments were conducted with WRF at 10 km resolution for the year 2013. The simulations differ in terms of configuration options such as the parameterization schemes, the hydrostatic and non-hydrostatic dynamical cores, the initial and boundary conditions (ERA5 and ERA-Interim reanalyses), the number of vertical levels, and the length of the spin-up period. E-OBS dataset 2 m temperature, total precipitation, and global radiation are used for validation. Temperature underestimation reaches 4–7 °C for some experiments and can be reduced by certain physics scheme combinations. The cold bias in winter and spring is mainly caused by excessive snowfall and too persistent snow cover, as revealed by comparison with satellite-based observations and a test simulation without snow on the surface. Annual precipitation is overestimated by 0.6–3.8 mm day−1, with biases mainly accumulating in the period driven by large-scale weather processes. Downward shortwave radiation is underestimated all year except in the months dominated by locally forced phenomena (May to August) when a positive bias prevails. The incorporation of downward shortwave radiation to the validation variables increased the understanding of underlying problems with the parameterization schemes and highlighted false model error compensations.

scholarly journals Regional climate hindcast simulations within EURO-CORDEX: evaluation of a WRF multi-physics ensemble

2015 ◽  
Vol 8 (3) ◽  
pp. 603-618 ◽  
Author(s):  
E. Katragkou ◽  
M. García-Díez ◽  
R. Vautard ◽  
S. Sobolowski ◽  
P. Zanis ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Water Cycle ◽  
Regional Climate ◽  
Shortwave Radiation ◽  
Cold Bias ◽  
Total Cloud Cover ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Downward Shortwave Radiation ◽  
Downward Radiation

Abstract. In the current work we present six hindcast WRF (Weather Research and Forecasting model) simulations for the EURO-CORDEX (European Coordinated Regional Climate Downscaling Experiment) domain with different configurations in microphysics, convection and radiation for the time period 1990–2008. All regional model simulations are forced by the ERA-Interim reanalysis and have the same spatial resolution (0.44°). These simulations are evaluated for surface temperature, precipitation, short- and longwave downward radiation at the surface and total cloud cover. The analysis of the WRF ensemble indicates systematic temperature and precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons. Overestimation of total cloud cover and underestimation of downward shortwave radiation at the surface, mostly linked to the Grell–Devenyi convection and CAM (Community Atmosphere Model) radiation schemes, intensifies the negative bias in summer temperatures over northern Europe (max −2.5 °C). Conversely, a strong positive bias in downward shortwave radiation in summer over central (40–60%) and southern Europe mitigates the systematic cold bias over these regions, signifying a typical case of error compensation. Maximum winter cold biases are over northeastern Europe (−2.8 °C); this location suggests that land–atmosphere rather than cloud–radiation interactions are to blame. Precipitation is overestimated in summer by all model configurations, especially the higher quantiles which are associated with summertime deep cumulus convection. The largest precipitation biases are produced by the Kain–Fritsch convection scheme over the Mediterranean. Precipitation biases in winter are lower than those for summer in all model configurations (15–30%). The results of this study indicate the importance of evaluating not only the basic climatic parameters of interest for climate change applications (temperature and precipitation), but also other components of the energy and water cycle, in order to identify the sources of systematic biases, possible compensatory or masking mechanisms and suggest pathways for model improvement.

Impact of a Dense Surface Network on High-Resolution Dynamical Downscaling via Observation Nudging

2020 ◽  
Vol 59 (10) ◽  
pp. 1655-1670
Author(s):  
Xue Yi ◽  
Deqin Li ◽  
Chunyu Zhao ◽  
Lidu Shen ◽  
Xiaoyu Zhou
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Wrf Model ◽  
Small Scale ◽  
Cold Bias ◽  
Surface Network ◽  
Density Surface ◽  
Surface Observations

AbstractHigh-density surface networks have become available in recent years in a number of regions throughout the world, but their utility in high-resolution dynamic downscaling has not been examined. As an attempt to fill such a gap, a suite of high-resolution (4 km) dynamical downscaling simulations is developed in this study with the Weather Research and Forecasting (WRF) Model and observation nudging over Liaoning in northeastern China. Three experiments, including no nudging (CTL), analysis nudging (AN), and combined analysis nudging and observation nudging with surface observations (AON), are conducted to downscale the CFSv2 reanalysis with the WRF Model for the year 2015. The three 1-yr regional climate simulations were compared with the independent surface observations. The results show that observational nudging can improve the simulation of surface variables, including temperature, wind speed, humidity, and pressure, more than nudging large-scale driving data with AN alone. The two nudging simulations can improve the cold bias for the temperature of the WRF Model. For precipitation, both the simulations with AN and observation nudging can capture the pattern of precipitation; however, with the introduction of small-scale information at the surface, AON cannot further improve the simulation of precipitation.

Hydrological evaluation of high-resolution precipitation estimates from the WRF model in the Third Pole river basins

2021 ◽  
Author(s):  
He Sun ◽  
Fengge Su ◽  
Zhihua He ◽  
Tinghai Ou ◽  
Deliang Chen ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Infiltration Capacity ◽  
Flood Prediction ◽  
Daily Streamflow ◽  
Precipitation Estimates

AbstractIn this study, two sets of precipitation estimates based on the regional Weather Research and Forecasting model (WRF) –the high Asia refined analysis (HAR) and outputs with a 9 km resolution from WRF (WRF-9km) are evaluated at both basin and point scales, and their potential hydrological utilities are investigated by driving the Variable Infiltration Capacity (VIC) large-scale land surface hydrological model in seven Third Pole (TP) basins. The regional climate model (RCM) tends to overestimate the gauge-based estimates by 20–95% in annual means among the selected basins. Relative to the gauge observations, the RCM precipitation estimates can accurately detect daily precipitation events of varying intensities (with absolute bias < 3 mm). The WRF-9km exhibits a high potential for hydrological application in the monsoon-dominated basins in the southeastern TP (with NSE of 0.7–0.9 and bias of -11% to 3%), while the HAR performs well in the upper Indus (UI) and upper Brahmaputra (UB) basins (with NSE of 0.6 and bias of -15% to -9%). Both the RCM precipitation estimates can accurately capture the magnitudes of low and moderate daily streamflow, but show limited capabilities in flood prediction in most of the TP basins. This study provides a comprehensive evaluation of the strength and limitation of RCMs precipitation in hydrological modeling in the TP with complex terrains and sparse gauge observations.

scholarly journals Evaluation of Upper Indus Near-Surface Climate Representation by WRF in the High Asia Refined Analysis

2019 ◽  
Vol 20 (3) ◽  
pp. 467-487 ◽  
Author(s):  
David M. W. Pritchard ◽  
Nathan Forsythe ◽  
Hayley J. Fowler ◽  
Greg M. O’Donnell ◽  
Xiao-Feng Li
Keyword(s):  
Regional Climate ◽  
Critical Role ◽  
Critical Evaluation ◽  
Longwave Radiation ◽  
Indus Basin ◽  
Shortwave Radiation ◽  
Cold Bias ◽  
Near Surface ◽  
High Asia ◽  
High Bias

Abstract Data paucity is a severe barrier to the characterization of Himalayan near-surface climates. Regional climate modeling can help to fill this gap, but the resulting data products need critical evaluation before use. This study aims to extend the appraisal of one such dataset, the High Asia Refined Analysis (HAR). Focusing on the upper Indus basin (UIB), the climatologies of variables needed for process-based hydrological and cryospheric modeling are evaluated, leading to three main conclusions. First, precipitation in the 10-km HAR product shows reasonable correspondence with most in situ measurements. It is also generally consistent with observed runoff, while additionally reproducing the UIB’s strong vertical precipitation gradients. Second, the HAR shows seasonally varying bias patterns. A cold bias in temperature peaks in spring but reduces in summer, at which time the high bias in relative humidity diminishes. These patterns are concurrent with summer overestimation (underestimation) of incoming shortwave (longwave) radiation. Finally, these seasonally varying biases are partly related to deficiencies in cloud, snow, and albedo representations. In particular, insufficient cloud cover in summer leads to the overestimation of incoming shortwave radiation. This contributes to the reduced cold bias in summer by enhancing surface warming. A persistent high bias in albedo also plays a critical role, particularly by suppressing surface heating in spring. Improving representations of cloud, snow cover, and albedo, and thus their coupling with seasonal climate transitions, would therefore help build upon the considerable potential shown by the HAR to fill a vital data gap in this immensely important basin.

scholarly journals Convection-Permitting Regional Climate Simulations Over Tibetan Plateau: Re-Initialization Versus Spectral Nudging

2021 ◽  
Author(s):  
Mengnan Ma ◽  
Pinhong Hui ◽  
Dongqing Liu ◽  
Peifeng Zhou ◽  
Jianping Tang
Keyword(s):  
Tibetan Plateau ◽  
Regional Climate ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Moisture Flux ◽  
Early Morning ◽  
Climate Simulation ◽  
Cold Bias ◽  
The Tibetan Plateau ◽  
Spectral Nudging

Abstract Two regional climate simulation experiments (spectral nudging and re-initialization) at convection-permitting scale are conducted using the WRF model over the Tibetan Plateau (TP). The surface air temperature (T2m) and the precipitation in summer during 2016–2018 are evaluated against the in-situ station observations and the Global Satellite Mapping of Precipitation (GSMaP) dataset. The results show that both experiments can successfully capture the spatial distribution and the daily variation of T2m and precipitation, with reasonable cold bias for temperature, dry bias for precipitation when compared with the station observations. In addition, the diurnal cycle of precipitation is investigated, indicating that both experiments tend to simulate the afternoon precipitation in advance and postpone the night precipitation. The precipitation bias is reduced by using the spectral nudging technique, especially at night and early morning. Possible causes for the differences between the two experiments are also analyzed. The daytime surface net radiation contributes a lot to the cold biases in the re-initialization experiment, and the stronger low-level moisture flux convergence leads to the wet biases. These results can provide valuable guidance for further fine-scale simulation studies over the TP.

scholarly journals An observation-constrained multi-physics WRF ensemble for simulating European mega heat waves

2015 ◽  
Vol 8 (7) ◽  
pp. 2285-2298 ◽  
Author(s):  
A. I. Stegehuis ◽  
R. Vautard ◽  
P. Ciais ◽  
A. J. Teuling ◽  
D. G. Miralles ◽  
...  
Keyword(s):  
Heat Wave ◽  
Land Surface ◽  
Western Europe ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Regional Climate ◽  
Shortwave Radiation ◽  
Cold Bias ◽  
The Impact

Abstract. Many climate models have difficulties in properly reproducing climate extremes, such as heat wave conditions. Here we use the Weather Research and Forecasting (WRF) regional climate model with a large combination of different atmospheric physics schemes, in combination with the NOAH land-surface scheme, with the goal of detecting the most sensitive physics and identifying those that appear most suitable for simulating the heat wave events of 2003 in western Europe and 2010 in Russia. In total, 55 out of 216 simulations combining different atmospheric physical schemes have a temperature bias smaller than 1 °C during the heat wave episodes, the majority of simulations showing a cold bias of on average 2–3 °C. Conversely, precipitation is mostly overestimated prior to heat waves, and shortwave radiation is slightly overestimated. Convection is found to be the most sensitive atmospheric physical process impacting simulated heat wave temperature across four different convection schemes in the simulation ensemble. Based on these comparisons, we design a reduced ensemble of five well performing and diverse scheme configurations, which may be used in the future to perform heat wave analysis and to investigate the impact of climate change during summer in Europe.

scholarly journals Differences between downscaling with spectral and grid nudging using WRF

2012 ◽  
Vol 12 (8) ◽  
pp. 3601-3610 ◽  
Author(s):  
P. Liu ◽  
A. P. Tsimpidi ◽  
Y. Hu ◽  
B. Stone ◽  
A. G. Russell ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Wrf Model ◽  
Global Climate Models ◽  
Small Scale ◽  
Data Set ◽  
Spectral Nudging

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

scholarly journals Impacts of Saharan Dust on Atlantic Regional Climate and Implications for Tropical Cyclones

2018 ◽  
Vol 31 (18) ◽  
pp. 7621-7644 ◽  
Author(s):  
Bowen Pan ◽  
Yuan Wang ◽  
Jiaxi Hu ◽  
Yun Lin ◽  
Jen-Shan Hsieh ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Wind Shear ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Regional Climate ◽  
Vertical Wind Shear ◽  
Saharan Dust ◽  
Specific Humidity ◽  
Shortwave Radiation ◽  
Cloud Formation

The radiative and microphysical properties of Saharan dust are believed to impact the Atlantic regional climate and tropical cyclones (TCs), but the detailed mechanism remains uncertain. In this study, atmosphere-only simulations are performed from 2002 to 2006 using the Community Atmospheric Model, version 5.1, with and without dust emission from the Sahara Desert. The Saharan dust exhibits noticeable impacts on the regional longwave and shortwave radiation, cloud formation, and the convective systems over West Africa and the tropical Atlantic. The African easterly jet and West African monsoon are modulated by dust, leading to northward shifts of the intertropical convergence zone and the TC genesis region. The dust events induce positive midlevel moisture and entropy deficit anomalies, enhancing the TC genesis. On the other hand, the increased vertical wind shear and decreased low-level vorticity and potential intensity by dust inhibit TC formation in the genesis region. The ventilation index shows a decrease in the intensification region and an increase in the genesis region by dust, corresponding to favorable and unfavorable TC activities, respectively. The comparison of nondust scenarios in 2005 and 2006 shows more favorable TC conditions in 2005 characterized by higher specific humidity and potential intensity, but lower ventilation index, wind shear, and entropy deficit. Those are attributable to the observed warmer sea surface temperature (SST) in 2005, in which dust effects can be embedded. Our results imply significant dust perturbations on the radiative budget, hydrological cycle, and large-scale environments relevant to TC activity over the Atlantic.

scholarly journals Orography-Induced Gravity Wave Drag Parameterization in the Global WRF: Implementation and Sensitivity to Shortwave Radiation Schemes

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Hyeyum Hailey Shin ◽  
Song-You Hong ◽  
Jimy Dudhia ◽  
Young-Joon Kim
Keyword(s):  
Gravity Wave ◽  
Wrf Model ◽  
Sensitivity Study ◽  
Wave Drag ◽  
Shortwave Radiation ◽  
Radiative Transfer Model ◽  
Weather Research And Forecasting ◽  
Cold Bias ◽  
Transfer Model ◽  
The Impact

This paper describes the implementation of the orographic gravity wave drag (GWDO) processes induced by subgrid-scale orography in the global version of the Weather Research and Forecasting (WRF) model. The sensitivity of the model simulated climatology to the representation of shortwave radiation and the addition of the GWDO processes is investigated using the Kim-Arakawa GWDO parameterization and the Goddard, RRTMG (Rapid Radiative Transfer Model for GCMs), and Dudhia shortwave radiation schemes. This sensitivity study is a part of efforts of selecting the physics package that can be useful in applying the WRF model to global and seasonal configuration. The climatology is relatively well simulated by the global WRF; the zonal mean zonal wind and temperature structures are reasonably represented with the Kim-Arakawa GWDO scheme using the Goddard and RRTMG shortwave schemes. It is found that the impact of the shortwave radiation scheme on the modeled atmosphere is pronounced in the upper atmospheric circulations above the tropopause mainly due to the ozone heating. The scheme that excludes the ozone process suffers from a distinct cold bias in the stratosphere. Moreover, given the improper thermodynamic environment conditions by the shortwave scheme, the role of the GWDO process is found to be limited.

scholarly journals Evaluation of convective parameters derived from pressure level and native ERA5 data and different resolution WRF climate simulations over Central Europe

2021 ◽  
Author(s):  
Ákos János Varga ◽  
Hajnalka Breuer
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Environmental Parameters ◽  
Pressure Level ◽  
Lapse Rate ◽  
Strong Suppression ◽  
Climate Simulations ◽  
Multiple Variables

AbstractThe mean climatological distribution of convective environmental parameters from the ERA5 reanalysis and WRF regional climate simulations is evaluated using radiosonde observations. The investigation area covers parts of Central and Eastern Europe. Severe weather proxies are calculated from daily 1200 UTC sounding measurements and collocated ERA5 and WRF pseudo-profiles in the 1985–2010 period. The pressure level and the native ERA5 reanalysis, and two WRF runs with grid spacings of 50 and 10 km are verified. ERA5 represents convective parameters remarkably well with correlation coefficients higher than 0.9 for multiple variables and mean errors close to zero for precipitable water and mid-tropospheric lapse rate. Monthly mean mixed-layer CAPE biases are reduced in the full hybrid-sigma ERA5 dataset by 20–30 J/kg compared to its pressure level version. The WRF model can reproduce the annual cycle of thunderstorm predictors but with considerably lower correlations and higher errors than ERA5. Surface elevation differences between the stations and the corresponding grid points in the 50-km WRF run lead to biases and false error compensations in the convective indices. The 10-km grid spacing is sufficient to avoid such discrepancies. The evaluation of convection-related parameters contributes to a better understanding of regional climate model behavior. For example, a strong suppression of convective activity might explain precipitation underestimation in summer. A decreasing correlation of WRF-derived wind shear away from the western domain boundaries indicates a deterioration of the large-scale circulation as the constraining effect of the driving reanalysis weakens.

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