Andrew S. Jones

Mesoscale Cloud State Estimation from Visible and Infrared Satellite Radiances

Abstract This study focuses on cloudy atmosphere state estimation from high-resolution visible and infrared satellite remote sensing measurements and a mesoscale model with explicit cloud prediction. The cloud state is defined as 3D spatially distributed hydrometeors characterized with microphysical properties: mixing ratio, number concentration, and size distribution. The Geostationary Operational Environmental Satellite-9 (GOES-9) imager visible and infrared measurements were used in a new four-dimensional variational data assimilation (4DVAR) mesoscale algorithm for a warm continental stratus cloud system case to test the impact of these observations on the cloud simulation. The new data assimilation algorithm includes the Regional Atmospheric Modeling System (RAMS) with explicit cloud state prediction, the associated adjoint system, and an observational operator for forward and adjoint integrations of the GOES radiances. The results show positive impact of GOES imager measurements on the 3D cloud shor...

Data assimilation of satellite-derived heating rates as proxy surface wetness data into a regional atmospheric mesoscale model. Part II : A case study

Abstract A satellite data assimilation method is applied which incorporates satellite-observed heating infrared rates into a mesoscale atmospheric model to retrieve model soil moisture. In a 3D case study, the method is successful at retrieving realistic spatial representations of the heterogeneous surface soil moisture as compared to microwave surface emissivities, precipitation reports, and radar summaries; however, absolute magnitudes of the derived soil moisture fields are by nature model dependent. From noise sensitivity experiments, satellite instrument noise is not found to be a major factor in the data assimilation method’s performance. The case study presented here over the Great Plains region showed a significant soil moisture gradient where a weak dryline feature formed in the afternoon. The main effect of the soil moisture gradient was a tightening of the water vapor gradient in the boundary layer. However, it was found that this feature was much less important than latent heat release due to ...

Error estimates of spaceborne passive microwave retrievals of cloud liquid water over land

Cloud liquid water path (LWP) retrievals from the Special Sensor Microwave/Imager (SSM/I) and surface microwave radiometers are compared over land to assess the errors in selected satellite methods. These techniques require surface emissivity composites created from SSM/I and infrared (IR) data. Two different physical methods are tested: a single-channel (SC) approach (either 85.5-GHz channel); and a normalized polarization difference (NPD) approach (37 or 85.5 GHz). Comparisons were made at four sites in Oklahoma and Kansas over an 11-month period. The 85.5-GHz NPD method was the most accurate and robust under most conditions. An error analysis shows that the methods random errors are dominated by uncertainties in the surface emissivity and instrument noise. Since the SC method is more prone to systematic errors (such as surface emissivity errors caused by rain events), it initially compared poorly to the ground observations. After filtering for rain events, the comparisons improved. Overall, the root mean square (rms) errors ranged from 0.12 to 0.14 kg m/sup -2/, suggesting these methods can provide, at best, three categories of cloud LWP. It is anticipated that the techniques and strategies developed in this study, and prior related studies, to analyze passive microwave data will be requisite for maximizing the information content of future instruments.

Analysis of Information Content of Infrared Sounding Radiances in Cloudy Conditions

Abstract Information content analysis of the Geostationary Operational Environmental Satellite (GOES) sounder observations in the infrared was conducted for use in satellite data assimilation. Information content is defined as a first-order response of the top-of-atmosphere brightness temperature to perturbations of simulated temperature and humidity profiles, obtained from a cloud-resolving model, both in the presence and absence of clouds. Sensitivity to the perturbations was numerically evaluated using an observational operator for visible and infrared radiative transfer developed within a research satellite data assimilation system. The vertical distribution of the sensitivities was analyzed as a function of cloud optical thickness covering the range from a cloud-free scene to an optically thick cloud. The clear-sky sensitivities to temperature and humidity perturbations for each channel are representative of the corresponding channel weighting functions for a clear-sky case. For optically thin–modera...

NPOESS soil moisture satellite data assimilation: Progress using WindSat data

In this work, we have developed a four-dimensional coupled atmospheric/land data assimilation framework using the Regional Atmospheric Modeling Data Assimilation System (RAMDAS) to retrieve deep soil moisture profiles. Passive microwave data from CORIOLIS WindSat are used as a surrogate for future National Polar-orbiting Operational Environmental Satellite System (NPOESS) microwave sensors. Current efforts are focused on the use of the system for a case study occurring in September 2003.

NPOESS Soil Moisture Satellite Data Assimilation and RFI Mitigation: Use of WindSat Data and a Discrete Backus-Gilbert Technique

This work develops (1) a four-dimensional data assimilation methodology to retrieve deep soil moisture profiles using the National Polar-orbiting Operational Environmental Satellite System (NPOESS) and other associated data, (2) a methodology for better spatial mapping of the masking effects caused by surface features (i.e., vegetative cover and surface roughness), and (3) a discrete Backus-Gilbert (DBG)-based methodology for reducing the radio frequency interference impacts at 6.7 and 10 GHz.

Development of a Global Microwave Land Surface Emissivity Retrieval

Microwave remote sensing over land has lagged behind remote sensing over oceans. This is due to the larger land emissivity values and their changes on daily to seasonal timescales. The lack of surface emissivity knowledge has hindered full exploitation of the capabilities of operational satellite sensors such as AMSU and SSM/I in remote sensing and data assimilation. Microwave land surface models that can be used to predict the emissivity have been developed and show promise, but independent global measurements of emissivity are needed for comparison. We have developed an optimal estimation retrieval, demonstrated with AMSU data, which retrieves global emissivity at frequencies from 23 to 183 GHz. A simultaneous retrieval of the temperature and water vapor profiles as well as cloud liquid water is performed. The simultaneous retrieval allows the masking effects of water vapor and some clouds to be reduced. The method does not currently use infrared data as a surface temperature constraint in clear sky regions. Initial comparisons with the NOAA NESDIS Microwave Emissivity Model are encouraging. The retrieval provides an independent estimate of emissivity, which is especially useful over difficult surface types such as snow and ice. These early results point the way to the creation of dynamic global emissivity fields which have applications to satellite microwave data assimilation, remote sensing of soil moisture, and future microwave sensors.

Microwave surface emittance retrieval using coincident microwave and infrared satellite data

Two methods are used to calculate microwave surface emittance using the Special Sensor Microwave/Imager (SSM/I) data and the GOES Visible Infrared Spin Scan Radiometer (VISSR) data. The first method calculates an effective surface emittance using only the microwave and infrared brightness temperatures and uses the infrared data as a near-blackbody temperature reference. This method does not account for atmospheric attenuation in the infrared or microwave frequencies. A second more rigorous method includes an atmospheric correction for water vapor attenuation. Comparisons are made between the methods followed by a discussion of the practical implications of the results.

Measurement Of High Resolution Microwave Surface Emittance Using Advanced Multisensor Data Fusion Techniques

Multisensor satellite data applications require a tremendous investment regarding data preparation and analysis. Advanced software processing techniques used in the Polar Orbiter Remapping and Transformation Application Library (PORTAL) (Jones and Vonder Haar, 1992) are used to show how polar DMSP SSM/I microwave data and geostationary GOES VISSR infrared data can be combined to measure the microwave surface emittance at the full spatial sensor resolution. In addition, the infrared data is also used to cloud-clear the microwave surface emittance results.