Combining satellite observations with a virtual ground-based remote sensing network for monitoring atmospheric stability

Atmospheric stability plays an essential role in the evolution of weather events. While the upper troposphere is sampled by satellite sensors, and in-situ sensors measure the atmospheric state close to the surface, only sporadic information from radiosondes or aircraft observations is available in the planetary boundary layer. Ground-based remote sensing offers the possibility to continuously and automatically monitor the atmospheric state in the boundary layer. Microwave radiometers (MWR) provide temporally resolved temperature and humidity profiles in the boundary layer and accurate values of integrated water vapor and liquid water path, while the DIfferential Absorption Lidar (DIAL) measures humidity profiles with high vertical and temporal resolution up to 3000 m height. Both instruments have the potential to complement satellite observations by additional information from the lowest atmospheric layers, particularly under cloudy conditions. The main objective of this work is to investigate the potential of ground-based and satellite sensors, as well as their synergy, for monitoring atmospheric stability. The first part of the study represents a neural network retrieval of stability indices, integrated water vapor, and liquid water path from simulated satellite- and ground-based measurements based on the reanalysis COSMO-REA2. The satellite-based instruments considered in the study are the currently operational Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the future Infrared Sounder (IRS), both in geostationary orbit, and the Advanced Microwave Sounding Unit (AMSU-A) and Infrared Atmospheric Sounding Interferometer (IASI), both deployed on polar orbiting satellites. Compared to the retrieval based on satellite observations, the additional ground-based MWR/DIAL measurements provide valuable improvements not only in the presence of clouds, which represent a limiting factor for infrared SEVIRI, IRS, and IASI, but also under clear sky conditions. The root-mean-square error for Convective Available Potential Energy (CAPE), for instance, is reduced by 24% if IRS observations are complemented by ground-based MWR measurements. The second part represents an attempt to assess the representativeness of observations of a single ground-based MWR and the impact of a network of MWR if combined with future geostationary IRS measurements. For this purpose, the reanalysis fields (150*150 km) in the western part of Germany were used to simulate MWR and IRS observations and to develop a neural network retrieval of CAPE and Lifted Index (LI). Further analysis was performed in the space of retrieved parameters CAPE and LI. The impact of additional ground-based network observations was investigated in two ways. First, using spatial statistical interpolation method, the fields of CAPE/LI retrieved from IRS observations were merged with the CAPE/LI values from MWR network taking into account the corresponding error covariance matrices of both retrievals. Within this method, the contribution of a ground-based network consisting of a varying number of radiometers (from one to 25) was shown to be significant under cloudy conditions. The second approach mimics the assimilation of satellite and ground-based observations in the space of retrieved CAPE/LI fields. Assuming the persistence of atmospheric fields for a period of six hours, the CAPE/LI fields calculated from reanalysis were taken as a first guess in an assimilation step. Observations, represented by CAPE/LI fields obtained from satellite and ground-based measurements with +6 hours delay, were assimilated by spatial interpolation. Within this method, the added value of ground-based observations, if compared to satellite contribution, is highly dependent on the current weather situation, cloudiness, and the position of ground-based instruments. For CAPE, the synergy of ground-based MWR and satellite IRS observations is essential even under clear sky conditions, since both passive sensors can not capture atmospheric profiles, needed for calculation of CAPE, with sufficient accuracy. Whereas for LI, the assimilation of observations of 25 MWR distributed in the domain is equivalent to the assimilation of horizontally resolved IRS observations, indicating that in the presence of clouds, MWR observations could replace cloud-affected IRS measurements. Within both approaches, it could be shown that the contribution of ground-based observations is more pronounced under cloudy conditions and is most valuable for the first 25 sensors located in the domain

Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5)

An Algorithm for Retrieving Precipitable Water Vapor over Land Based on Passive Microwave Satellite Data

Precipitable water vapor (PWV) is one of the most variable components of the atmosphere in both space and time. In this study, a passive microwave-based retrieval algorithm for PWV over land without land surface temperature (LST) data was developed. To build the algorithm, two assumptions exist: (1) land surface emissivities (LSE) at two adjacent frequencies are equal and (2) there are simple parameterizations that relate transmittance, atmospheric effective radiating temperature, and PWV. Error analyses were performed using radiosonde sounding observations from Zhangye, China, and CE318 measurements of Dalanzadgad (43°34′37′′N, 104°25′8′′E) and Singapore (1°17′52′′N, 103°46′48′′E) sites from Aerosol Robotic Network (AERONET), respectively. In Zhangye, the algorithm had a Root Mean Square Error (RMSE) of 4.39 mm and a bias of 0.36 mm on cloud-free days, while on cloudy days there was an RMSE of 4.84 mm and a bias of 0.52 mm because of the effect of liquid water in clouds. The validations in Dalanzadgad and Singapore sites showed that the retrieval algorithm had an RMSE of 4.73 mm and a bias of 0.84 mm and the bigger errors appeared when the water vapor was very dry or very moist.</jats:p

A global climatology of total columnar water vapour from SSM/I and MERIS

A global time series of total columnar water vapour from combined data of the Medium Resolution Imaging Spectrometer (MERIS) onboard ESA's Environmental Satellite (ENVISAT) and the Special Sensor Microwave/Imager (SSM/I) onboard the satellite series of the US Defense Meteorological Satellite Program (DMSP) is presented. The unique data set, generated in the framework of the ESA Data User Element (DUE) GlobVapour project, combines atmospheric water vapour observations over land and ocean, derived from measurements in the near- infrared and the microwave range, respectively. Daily composites and monthly means of total columnar water vapour are available as global maps on rectangular latitude–longitude grids with a spatial resolution of 0.05° × 0.05° over land and 0.5° × 0.5° over ocean for the years 2003 to 2008. The data are stored in NetCDF files and is fully compliant with the NetCDF Climate Forecast convention. Through the combination of high-quality microwave observations and near-infrared observations over ocean and land surfaces, respectively, the data set provides global coverage. The combination of both products is carried out such that the individual properties of the microwave and near-infrared products, in particular their uncertainties, are not modified by the merging process and are therefore well defined. Due to the global coverage and the provided uncertainty estimates this data set is potentially of high value for climate research. The SSM/I-MERIS TCWV data set is freely available via the GlobVapour project web page (www.globvapour.info) with associated doi:10.5676/DFE/WV_COMB/FP. In this paper, the details of the data set generation, i.e. the satellite data used, the retrieval techniques and merging approaches, are presented. The derived level 3 products are compared to global radiosonde data from the GCOS upper air network (GUAN), showing a high agreement with a root-mean-square deviation of roughly 4.4 kg m−2 and a small wet bias well below 1 kg m−2. Furthermore, the data set is shown to be free of seasonal biases. The consistency of the MERIS and SSM/I retrievals is demonstrated by applying the MERIS retrieval to sun glint areas over ocean

Laboratory for Atmospheres 2007 Technical Highlights

The 2007 Technical Highlights describes the efforts of all members of the Laboratory for Atmospheres. Their dedication to advancing Earth Science through conducting research, developing and running models, designing instruments, managing projects, running field campaigns, and numerous other activities, is highlighted in this report