Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach

The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging （BMA） ensemble approach. The simulations by the community microwave emission model （CMEM） cou- pled with the community land model version 4.5 （CLM4.5） over China's Mainland are con- ducted by the 24 configurations from four vegetation opacity parameterizations （VOPs）, three soil dielectric constant parameterizations （SDCPs）, and two soil roughness param- eterizations （SRPs）. Compared with the simple arithmetical averaging （SAA） method, the BMA reconstructions have a higher spatial correlation coefficient （larger than 0.99） than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system （AMSR-E） at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference （RMSD） of 4 K and a temporal correlation coefficient of 0.64.

Evaluation of the Community Microwave Emission Model Coupled with the Community Land Model over East Asia

The Community Microwave Emission Model (CMEM) developed by the European Centre for Me-dium-Range Weather Forecasts (ECMWF) can provide a link between surface states and satellite observations and simulate the passive microwave brightness temperature of the surface at low frequencies (from 1 GHz to 20 GHz).This study evaluated the performance of the CMEM cou-pled with the Community Land Model (CLM) (CMEM-CLM) using C-band (6.9 GHz) microwave brightness temperatures from the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E) over East Asia.Preliminary results support the argument that the simulated brightness temperatures of CMEM-CLM from July 2005 to June 2010 are comparable to AMSR-E observational data.CMEM-CLM performed better for vertical polarization,for which the root mean square error was approximately 15 K,compared to over 30 K for horizontal polarization.An evaluation performed over seven sub-regions in China indicated that CMEM-CLM was able to capture the temporal evolution of C-band brightness temperatures well,and the best correlation with AMSR-E appeared over western Northwest China (over 0.9 for vertical polarization).However,larger biases were found over southern North China and the middle and lower reaches of the Yangtze River.

Sea surface wind speed retrieval under rain with the HY-2 microwave radiometer

As rain drops change the radiation and scattering characteristic of the oceans and the atmosphere, the wind speed measuring by spaceborne remote sensors under rainy conditions remains challenging for years. On the basis of a microwave radiometer（RM） loaded on HY-2 satellite, the sensitivity of some brightness temperature（TB）channels to a rain rate and the wind speed are analyzed. Consequently, two TB combinations which show minor sensitivity to rain are obtained. Meanwhile, the sensitivity of the TB combination to the wind speed is even better to the original TB channel. On the basis of these TB combinations, a wind speed retrieval algorithm is developed and compared with Wind Sat all-weather wind speed product, HY-2 RM original wind speed product and buoy in situ data. The wind speed retrieval accuracy is better than 2 m/s for rainy conditions, which is evidently superior to HY-2 RM original product. The applicability of this new algorithm is testified for the wind speed measuring in rainy weather with HY-2 RM.

Evaluating effects of Dielectric Models on the surface soil moisture retrieval in the Qinghai-Tibet Plateau

Based on the measurement of L-band ground-based microwave radiometer(ELBARA-III type)in the Qinghai-Tibet Plateau and theτ-ωradiative transfer model,this research evaluated the effects of four soil dielectric models,i.e.,Wang-Schmugge,Mironov,Dobson,and Four-phase,on the L-band microwave brightness temperature simulation and soil moisture retrieval.The results show that with the same vegetation and roughness parameterization scheme,the four soil dielectric models display obvious differences in microwave brightness temperature simulation.When the soil moisture is less than 0.23 m3/m3,the simulated microwave brightness temperature in Wang-Schmugge model is significantly different from that of the other three models,with maximum differences of horizontal polarization and vertical polarization reaching 8.0 K and 4.4 K,respectively;when the soil moisture is greater than 0.23 m3/m3,the simulated microwave brightness temperature of Four-phase significantly exceeds that of the other three models;when the soil moisture is saturated,maximum differences in simulated microwave brightness temperature with horizontal polarization and vertical polarization are 6.1 K and 4.8 K respectively,and the four soil dielectric models are more variable in the microwave brightness temperature simulation with horizontal polarization than that with vertical polarization.As for the soil moisture retrieval based on the four dielectric models,the comparison study shows that,under the condition of horizontal polarization,Wang-Schmugge model can reduce the degree of retrieved soil moisture underestimating the observed soil moisture more effectively than other parameterization schemes,while under the condition of vertical polarization,the Mironov model can reduce the degree of retrieved soil moisture overestimating the observed soil moisture.Finally,based on the Wang-Schmugge model and FengYun-3C observation data,the spatial distribution of soil moisture in the study area is retrieved.

Exploring the influence of various factors on microwave radiation image simulation for Moon-based Earth observation

Earth observation technologies are important for obtaining geospatial information on the Earth’s surface and are used widely in many disciplines,such as resource surveying,environmental monitoring,and evolutionary studies.However,it is a challenge for existing Earth observation platforms to acquire this type of data rapidly on a global scale due to limitations in orbital altitude and field of view;thus development of an advanced platform for Earth observation is desirable.As a natural satellite of the Earth,placement of various sensors on the Moon could possibly facilitate comprehensive,continuous,and longterm observations of the Earth.This is a relatively new concept and the study is still at the preliminary stage with no actual Moon-based Earth observation data available at this time.To understand the characteristics of Moon-based microwave radiation,several physical factors that potentially influence microwave radiation imaging,e.g.,time zone correction,relative movement of the Earth-Moon,atmospheric radiative transfer,and the effect of the ionosphere,were examined.Based on comprehensive analysis of these factors,the Moon-based microwave brightness temperature images were simulated using spaceborne temperature data.The results show that time zone correction ensures that the simulation images may be obtained at Coordinated Universal Time(UTC)and that the relative movement of the Earth-Moon affects the positions of the nadir and Moon-based imaging.The effect of the atmosphere on Moon-based observation is dependent on various parameters,such as atmospheric pressure,temperature,humidity,water vapor,carbon dioxide,oxygen,the viewing zenith angle and microwave frequency.These factors have an effect on atmospheric transmittance and propagation of upward and downward radiation.When microwaves propagate through the ionosphere,the attenuation is related to frequency and viewing zenith angle.Based on initial studies,the simulation results suggest Moon-based microwave radiation imaging is realistic and viable.

Variations of Precipitation Structure and Microwave Tbs During the Evolution of a Hailstorm from TRMM Observations

In this paper, a hailstorm occurring on 9 May 1999 in Huanghuai region was studied by using the combined data from the precipitation radar （PR）, microwave image （TMI）, and visible infrared scanner （VIRS） on the Tropical Rainfall Measuring Mission （TRMM） satellite. According to the 3-orbit observations of 5- h duration from the TRMM satellite, the variation characteristics of the precipitation structures as well as cloud top temperature and microwave signals of the precipitating cloud were comprehensively analyzed during the evolution of hailstorm. The results show that the precipitation is obviously converted from early hail cloud with strong convection into the later storm cloud with weak convection. For hail cloud, there exists some strong convective cells, and the heavy solid precipitation is shown at the middle-top levels so that the contribution of rainfall amount above the freezing-layer to the column precipitation amount is rather larger than that within the melting-layer. However, for storm cloud, the convective cells are surrounded by the large area of stratiform precipitation, and the precipitation thickness gradually decreases, and the rainfall above the freezing-layer obviously reduces and the contribution of rainfall amount within the melting-layer rapidly increases. Therefore, the larger ratio of rainfall amount above the freezing layer to column precipitation amount is, the more convective the cloud is; reversely, the larger proportion of rainfall below the melting layer is, the more stable the stratiform cloud is. The different changing trends of microwave signals at different precipitation stages show that it is better to consider the structures and stages of precipitating cloud to choose the optimal microwave channels to retrieve surface rainfall.

Simultaneous estimation of soil moisture and hydraulic parameters using residual resampling particle filter

Land data assimilation(DA) has gradually developed into an important earth science research method because of its ability to combine model simulations and observations.Integrating new observations into a land surface model by the DA method can correct the predicted trajectory of the model and thus,improve the accuracy of state variables.It can also reduce uncertainties in the model by estimating some model parameters simultaneously.Among the various DA methods,the particle filter is free from the constraints of linear models and Gaussian error distributions,and can be applicable to any nonlinear and non-Gaussian state-space model;therefore,its importance in land data assimilation research has increased.In this study,a DA scheme was developed based on the residual resampling particle filter.Microwave brightness temperatures were assimilated into the macro-scale semi-distributed variance infiltration capacity model to estimate the surface soil moisture and three hydraulic parameters simultaneously.Finally,to verify the scheme,a series of comparative experiments was performed with experimental data obtained during the Soil Moisture Experiment of 2004 in Arizona.The results show that the scheme can improve the accuracy of soil moisture estimations significantly.In addition,the three hydraulic parameters were also well estimated,demonstrating the effectiveness of the DA scheme.