The evaporation of the sea surface water causes significant changes in the vertical atmospheric environment, which in turn changes the refracting way of the radio wave. It affects the propagation path and the field strength of the radio wave, and has an important impact on the electronic systems such as radar and communication operating. However, there is still a lack of real-time and effective monitoring of the radio wave environment. Hyperspectral infrared data can provide a wide range of temperature and humidity profiles, which are the factors that directly affects refractive index calculation. In this study, regional assimilation of hyperspectral infrared radiances was carried out in a community assimilation system, using GSI coupled with the WRF model, to improve the retrieval of temperature and humidity profiles. The results show that the meteorological field after assimilation can effectively improve the accuracy of the atmospheric duct monitoring.
The accuracy of the temperature and humidity profiles is important for the atmospheric duct estimation, which is a special atmosphere layer for the radio-wave propagation. In order to use the dataset of satellite to monitor the atmospheric duct, we compare the temperature and humidity profiles between the radiosonde observation data (RAOB) and the NOAA-Unique CrIS/ATMS Product System (NUCAPS), and analyze the result of the atmospheric duct. Results show that the retrieved temperature and humidity profiles have higher accuracy under various weather conditions. However, when the RAOB data can calculate the atmospheric duct, the inversion profiles are difficult to monitor the same situation. The temperature inversion and humidity’s sharp decrease with height are the main synoptic conditions for the formation of atmospheric waveguides. Currently, the temperature and humidity profile of satellite inversion still lack capturing of turning point information. In order to effectively improve the application of satellite inversion data in atmospheric duct estimation, it is necessary to strengthen the profile’s vertical resolution and humidity inversion accuracy.
Hyperspectral infrared remote sensing can provide the information about temperature and humidity of the atmosphere at high vertical resolution and high accuracy. To assimilate its radiances directly, we must correct biases between the observed radiances and the simulated ones from the model first guess, caused by systematic error of radiances and by the radiative transfer model and assimilating system. The method used for bias correction was developed for global models, and its adaptation to regional models raises further questions. This study is based on coupling the mesoscale numerical model weather research and forecast and gridpoint statistical interpolation assimilation system, using adaptive variational bias correction (VarBC) to the scan angle and air-mass factor, and investigates the characteristics of bias correction coefficients for the regional model. It was found that advanced infrared sounder (AIRS) channels located in the 15-μm CO2 absorption band had large scan bias, and that its nadir bias had a time dependence, which is probably due to the bias from the radiative transfer model. By contrast, other channels had small scan bias and weak time dependence. In air-mass bias correction, predictors of zenith and temperature lapse rate had huge oscillations due to variations in data coverage from the regional models. The effect of this scheme on correction in a regional model was verified via the histogram analysis of innovation. The verification showed that correction on most of the channels got satisfactory results except for several land surface channels. The corrected histogram satisfied the requirement of an unbiased normal distribution. In a typhoon forecast experiment, the influence of radiance bias correction on forecast result was tested. It showed that, compared with parameters from the global model, regional radiance correction parameters study improved the prediction of the typhoon 72-h forecast.
The accuracy of the temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) is evaluated using three month of collocated datasets over East China. The AIRS/AMSU retrievals, radiosonde data (RAOB), and the ERA-Interim data from European Center for medium Range Forecast (ECMWF) are used in this validation. This study also compares the AIRS/AMSU retrieved profiles with it only retrieved by AIRS. Results of the entire intercomparison reveal that the RMSE of temperature profiles are in very good agreement with all cases, whilst the relative humidity RMSE show larger difference. Compared with RAOB for the AIRS/AMSU retrievals and ERA-Interim data, it is found that the ERA-Interim temperature and humidity profiles are superior to AIRS retrievals except the humidity in upper troposphere. The accuracy of AIRS/AMSU retrievals is a little bit better than only AIRS retrieved profile product.
A typical heavy rainfall event occurred in Shanghai on September 13, 2009 was simulated using the Weather Research and Forecasting Model (WRF) to study the impact of microphysics parameterization on heavy precipitation simulations. Sensitivity experiments were conducted using the cumulus parameterization scheme of Betts-Miller-Janjic (BMJ), but with three different microphysics schemes (Lin et al, WRF Single-Moment 5-class scheme (WSM5) and WRF Single-Moment 6-class scheme (WSM6)) under three-way nested domains with horizontal resolutions of 36km, 12km and 4km. The results showed that all three microphysics schemes are able to capture the general pattern of this heavy rainfall event, but differ in simulating the location, center and intensity of precipitation. Specifically, the Lin scheme overestimated the rainfall intensity and simulated the rainfall location drifting northeastwards. However, the WSM5 scheme better simulated the rainfall location but stronger intensity than the observation, while the WSM6 scheme better produced the rainfall intensity, but with an unrealistic rainfall area.
KEYWORDS: Clouds, Data modeling, Atmospheric modeling, Convection, Meteorology, Monte Carlo methods, Data centers, Computer simulations, Systems modeling, Physics
The Weather Research and Forecast Model (WRF) version 3.5 has been used in this study to simulate a heavy rainfall event during the Meiyu season that occurred between 1 and 2 July 2014 over the Yangtze River valley (YRV) in China. The WRF model is driven by the National Centers for Environmental Predictions (NCEP) Final (FNL) global tropospheric analysis data, and eight WRF nested experiments using four different microphysics (MP) schemes and two cumulus parameterizations (CP) are conducted to evaluate the effects of these microphysics and cumulus schemes on heavy rainfall predictions over YRV region. The four MPs selected in this study are Lin et al., WRF Single-Moment 3-class scheme (WSM3), WRF Single-Moment 5-class scheme (WSM5) and WRF Single-Moment 6-class scheme (WSM6), and the two CPs are Kain-Fristch (KF) and Betts-Miller-Janjic (BMJ) schemes. Sensitivity studies showed that all MPs coupling with KF and BMJ CP schemes can well capture the major rain belt from the northeast to southwest with three rainfall centers, but largely overestimate the rainfall near the border between Anhui and Hubei provinces along with the Yellow Sea shore, which produce an opposite trend compared to the observations. Large discrepancies are also presented in WRF simulations of heavy rainfall centers regarding their locations and magnitudes. All MPs coupling with KF CP scheme produced the rainfall areas shifting towards east compared to the observations, while all MPs with BMJ CP scheme tend to better predict the rainfall patterns with slightly more fake precipitation centers. Among all the experiments, the BMJ cumulus scheme has superiority in simulating the Meiyu rainfall over the KF scheme, and the WSM5–BMJ combination shows the best predictive skills.
KEYWORDS: Error analysis, Statistical analysis, Humidity, Data modeling, Atmospheric modeling, Systems modeling, Matrices, Statistical modeling, Control systems, Analytical research
Background error covariance (B) matrix is critical for variational data assimilation as it greatly affects the analyses of three-dimensional variational assimilation. The National Meteorological Center method was used to estimate the B matrix using the forecasts from the Advanced Research Weather Research and Forecasting regional model. To further understand and evaluate the newly generated regional B matrix, its characteristics were compared with the global B estimated from the Global Forecast System model. Sensitivity experiments were undertaken by changing the horizontal length-scales and standard deviations of the B matrix, and its impacts on the typhoon forecast were also examined. Verification against radiosonde observations showed that the varying horizontal length-scale has a significant positive impact on the 24-h forecast of temperature, specific humidity, u-wind, and v-wind. On the other hand, changing standard deviations of the B matrix has a slight influence only on the specific humidity and wind (u-component) forecast. Compared with the global B, the tuned regional B showed improvements in temperature forecasts. In addition, using the tuned regional B also led to a positive impact on the typhoon (Saola, Damrey, and Haikui) track forecasts as compared with the untuned B and global B.
Air quality has become a social issue that is causing great concern to humankind across the globe, but particularly in developing countries. Even though the Weather Research and Forecasting with Chemistry (WRF-Chem) model has been applied in many regions, the resolution for inputting meteorology field analysis still impacts the accuracy of forecast. This article describes the application of the CIMSS Regional Assimilation System (CRAS) in East China, and its capability to assimilate the direct broadcast (DB) satellite data for obtaining more detailed meteorological information, including cloud top pressure (CTP) and total precipitation water (TPW) from MODIS. Performance evaluation of CRAS is based on qualitative and quantitative analyses. Compared with data collected from ERA-Interim, Radiosonde, and the Tropical Rainfall Measuring Mission (TRMM) precipitation measurements using bias and Root Mean Square Error (RMSE), CRAS has a systematic error due to the impact of topography and other factors; however, the forecast accuracy of all elements in the model center area is higher at various levels. The bias computed with Radiosonde reveals that the temperature and geopotential height of CRAS are better than ERA-Interim at first guess. Moreover, the location of the 24 h accumulated precipitation forecast are highly consistent with the TRMM retrieval precipitation, which means that the performance of CRAS is excellent. In summation, the newly built Vtable can realize the function of inputting the meteorology field from CRAS output into WRF, which couples the CRAS with WRF-Chem. Therefore, this study not only provides for forecast accuracy of CRAS, but also increases the capability of running the WRF-Chem model at higher resolutions in the future.
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