Satellite Retrieval of a Strong Hailstorm Process

A case of hailstorm process occurring on 24 June 2006 in northwestern China was studied using satellite retrieval methodology. The particle effective radius (re) in the cloud tops was calculated by the reflectance in the 3.7 μm channel, and cloud-top microphysical properties were vividly represented using the RGB visual multispectral classification scheme. The microphysical zones of clouds and the processes of hail formation and develop-ment are inferred using the relations of cloud-top temperature (T) versus re for the tops of convective clouds. The results show that particle effective radius was smaller near the cloud base of hailstorm. There was a deep zone of diffusional droplet growth at the low level where the particles grew slowly with height, and there existed an evident area of small ice particles in the cloud top, suggesting the existence of a strong updraft in the clouds. The low glaciated temperature indicated a great depth from the cloud base to the glaciation height, which provided a deep layer of supercooled water for hail growth.

The Study of In-Orbit Calibration Accuracy of NOAA Satellite Infrared Sounder and Its Effect on Temperature Profile Retrievals

The calibration accuracy of High Resolution Infrared Radiation Sounder Mod. 2 (HIRS / 2) on NOAA-10 satellite is analyzed in this paper. The non-linear effect in the linear calibration curve induces a deviation of 1.5 degrees (k) of brightness temperature in the tenth channel (8.3 um, water vapor absorption) of the HIRS/2 and the non-linear effect affects the other channels to a different extent. Based on analyzing non- linearity in two-point calibration curve, a tri-point calibration equation is given. A numerical test of effects of the linear and non-linear calibration models on the accuracy of atmospheric temperature retrievals is carried out.

Verification and Correction of Cloud Base and Top Height Retrievals from Ka–band Cloud Radar in Boseong,Korea

In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（September-November） were verified and corrected.For comparative verification,CBH and CTH were obtained using a ceilometer（CL51） and the Communication,Ocean and Meteorological Satellite（COMS）.During rainfall,the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops,and this difference increased with rainfall intensity.During dry periods,however,the CBH and CTH observed by the cloud radar,ceilometer,and COMS were similar.Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS.In cases of rainfall or missing cloud radar data,the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data.These corrected cloud data were used to classify cloud types,which revealed that low clouds occurred most frequently.

Retrieval of Upper Tropospheric Relative Humidity by the GMS-5 Water Vapor Channel:A Study of the Technique

This paper presents an analysis of a technique for retrieving upper tropospheric relative humidity through the GMS-5 satellite's 6.7-micron water vapor channel brightness temperature. NCEP analysis shows that a critical assumption of the retrieval theory, namely the constant temperature lapse rate, matches only in the tropical atmosphere. By statistical analyses of brightness temperature simulated by a radiative transfer model and of relative humidity, we examine the effect of lapse rate on this retrieval method and obtain retrieval parameters and error estimates applicable to the GMS-5 satellite over East Asia. If the retrieval parameters are properly chosen, the relative error of retrieving the upper tropospheric relative humidity in this region is less than 10%, and if applied to the low-latitude summer atmosphere, it is less than 5%.

Monitoring Carbon Dioxide from Space：Retrieval Algorithm and Flux Inversion Based on GOSAT Data and Using CarbonTracker-China

Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of ＂The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change： Carbon Budget and Relevant Issues＂. The carbon flux inversion system is composed of two separate parts： the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing（IAPCAS）, and Carbon Tracker-China（CT-China）, developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite（GOSAT） measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 exchange is-0.34 Pg C yr^（-1）（±0.08 Pg C yr^（-1））, with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.

Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC （National Meteorological Center） method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.

An advanced carbon dioxide retrieval algorithm for satellite measurements and its application to GOSAT observations

An advanced carbon dioxide retrieval algo- rithm for satellite observations has been developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The algorithm is tested using Greenhouse gases Observing SATellite （GOSAT） LIB data and validated using the Total Column Carbon Observing Network （TCCON） measurements. The retrieved XCO2 agrees well with TCCON measurements in a low bias of 0.15 ppmv and RMSE of 1.48 ppmv, and captured the seasonal vari- ation and increasing of XCO2 in Northern and Southern Hemisphere, respectively, as other measurements.

Microphysical Effects of Cloud Seeding in Supercooled Stratiform Clouds Observed from NOAA Satellite

Based on the satellite retrieval methodology, the spectral characteristics and cloud microphysical properties were analyzed that included brightness temperatures of Channels 4 and 5, and their brightness temperature difference （BTD）, the particle effective radius of seeded cloud track caused by an operational cloud seeding and the microphysical effects of cloud seeding were revealed by the comparisons of their differences inside and outside the seeded track. The cloud track was actually a cloud channel reaching 1.5-km deep and 14-km wide lasting for more than 80 min. The effective radius of ambient clouds was 10-15 μm, while that within the cloud track ranged from 15 to 26 μm. The ambient clouds were composed of supercooled droplets, and the composition of the cloud within the seeding track was ice. With respect to the rather stable reflectance of two ambient sides around the track, the visible spectral reflectance in the cloud track varied at least 10%, and reached a maximum of 35%, the reflectance of 3.7 μm in the seeded track relatively decreased at least 10%. As cloud seeding advanced, the width and depth were gradually increased. Simultaneously the cloud top temperature within the track became progressively warmer with respect to the ambient clouds, and the maximum temperature differences reached 4.2 and 3.9℃ at the first seeding position for Channels 4 and 5. In addition, the BTD in the track also increased steadily to a maximum of 1.4℃, compared with 0.2-0.4℃ of the ambient clouds. The evidence that the seeded cloud became thinner comes from the visible image showing a channel, the warming of the cloud tops, and the increase of BTD in the seeded track. The seeded cloud became thinner mainly because the cloud top descended and it lost water to precipitation throughout its depth. For this cloud seeding case, the glaciation became apparent at cloud tops about 22 min after seeding. The formation of a cloud track in the supercooled stratiform clouds was mainly because that the seeded cloud volume glaciated into ice hydrometeors that precipitated and so lowered cloud top height. A thin line of new water clouds formed in the middle of the seeded track between 38 and 63 min after seeding, probably as a result of rising motion induced by the released latent heat of freezing. These clouds disappeared in the earlier segments of the seeded track, which suggested that the maturation of the seeding track was associated with its narrowing and eventual dissipation due to expansion of the tops of the ambient clouds from the sides inward.

Biological crust in sand and dust storm source areas of Asia and its impact on dust emission

Even though the biological crusts are critical to dust emissions,no sand and dust forecast model have considered the impacts of the biological crust in dust emission scheme.This situation mainly comes from two scientific difficulties:there is no large scale regional biological crust data available that can be used in the forecast model;there is no quantification of how biological crusts impact on sand emission.In this way,we studied the distribution of biological soil crust in sand and dust storm source areas of Central and East Asia using Moderate Resolution Imaging Spectroradiometer satellite surface reflectance data collected in 2000—2019 to determine its potential impact on dust emission according to two empirical schemes.We further evaluated the relationships between soil crust coverage,roughness length,and dust emission to study SDS source areas.We found that biological crust is widely distributed in SDS source areas of Central and East Asia,with coverage rates of 19.8%in Central Asian deserts,23.1%in the Gobi Desert,and 17.3%—32.8%in Chinese deserts(p>0.05).Cyanobacteria and lichen coverage has increased in Chinese deserts,reflecting the recent impacts of the Project of Returning Farmland to Grassland and Farmland to Forests.However,biological soil crust coverage has not increased in Central Asian deserts or the Gobi Desert,and that in Central Asian deserts continues to decrease,demonstrating the complexity of the combined effects of human activities and climate change on its distribution.Biological soil crust increased the roughness length of Central and East Asian SDS source areas by 0.14—0.62 mm.The suppression of dust emission due to biological soil crust did not change among years during the study period.The horizontal and vertical dust flux inhibition coefficient(DFIC)were 2.0—11.0 and 1.7—2.9(p>0.05),respectively,clearly showing a suppressive effect.Improvement of the ecological environment in some deserts can lead to the ability of these crusts to inhibit dust erosion errors that must be considered in the dust emission scheme for areas where crust coverage has improved.

Space- and ground-based CO_2 measurements： A review

The climate warming is mainly due to the increase in concentrations of anthropogenic greenhouse gases, of which CO_2 is the most important one responsible for radiative forcing of the climate. In order to reduce the great estimation uncertainty of atmospheric CO_2 concentrations, several CO_2-related satellites have been successfully launched and many future greenhouse gas monitoring missions are planned. In this paper, we review the development of CO_2 retrieval algorithms, spatial interpolation methods and ground observations. The main findings include: 1) current CO_2 retrieval algorithms only partially account for atmospheric scattering effects; 2) the accurate estimation of the vertical profile of greenhouse gas concentrations is a long-term challenge for remote sensing techniques; 3) ground-based observations are too sparse to accurately infer CO_2 concentrations on regional scales; and 4) accuracy is the primary challenge of satellite estimation of CO_2 concentrations. These findings, taken as a whole, point to the need to develop a high accuracy method for simulation of carbon sources and sinks on the basis of the fundamental theorem of Earth's surface modelling, which is able to efficiently fuse space- and ground-based measurements on the one hand and work with atmospheric transport models on the other hand.