Compressive Earth Observatory: An Insight from AIRS/AMSU Retrievals

We demonstrate that the global fields of temperature, humidity and geopotential heights admit a nearly sparse representation in the wavelet domain, offering a viable path forward to explore new paradigms of sparsity-promoting data assimilation and compressive recovery of land surface-atmospheric states from space. We illustrate this idea using retrieval products of the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) on board the Aqua satellite. The results reveal that the sparsity of the fields of temperature is relatively pressure-independent while atmospheric humidity and geopotential heights are typically sparser at lower and higher pressure levels, respectively. We provide evidence that these land-atmospheric states can be accurately estimated using a small set of measurements by taking advantage of their sparsity prior.Comment: 12 pages, 8 figures, 1 tabl

Evaluating the 3D EnKF - VAR Hybrid Data Assimilation in GSI for Surface and Upper Level Analyses

This study examines the 3 dimensional analysis produced using the Hybrid Ensemble Kalman Filter (EnKF) Variational (VAR) Data Assimilation in the Gridpoint Statistical Interpolation (GSI) System. The data assimilation ingests the 1 hour forecast High-Resolution Rapid Refresh (HRRR) and The Global Ensemble Forecast System, as the background and ensemble member set, respectively. Also, the conventional and satellite radiance observations are assimilated. The analysis covers a CONUS domain and has a 3 km horizontal resolution with 50 vertical native levels. The experiments focus on the advantages of using the flow dependentbackground error in the hybrid scheme to dynamically characterize the model background error based on the flow of the day. From the case study results, the hybrid scheme has a higher accuracy in 2m temperature and 10m winds speed than the background and the 3D VAR scheme, especially in regions of weather systems such as frontal boundaries and low pressure centers. Statistical comparisons of the surface analysis indicated the hybrid scheme outperformed the background and 3D VAR, but is unable to surpass the results from theReal TimeMesoscale Analysis (RTMA). Also, the impact of the flow dependentbackground error covariance in the hybrid scheme was compared with the terrain following background error covariance in the RTMA. Upper-level analysis comparison suggests the hybrid has a lower RMSE than the background and the 3D VAR for the lower and mid atmosphere but have similar results for the upper atmosphere. A brief sensitivity test on the vertical localization showed little impact on the upper-level analysis. Lastly, the benefit of assimilating satellite radiance observation and the performance of the enhanced radiance bias correction in GSI was examined

The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0

This report documents the GEOS-5 global atmospheric model and data assimilation system (DAS), including the versions 5.0.1, 5.1.0, and 5.2.0, which have been implemented in products distributed for use by various NASA instrument team algorithms and ultimately for the Modem Era Retrospective analysis for Research and Applications (MERRA). The DAS is the integration of the GEOS-5 atmospheric model with the Gridpoint Statistical Interpolation (GSI) Analysis, a joint analysis system developed by the NOAA/National Centers for Environmental Prediction and the NASA/Global Modeling and Assimilation Office. The primary performance drivers for the GEOS DAS are temperature and moisture fields suitable for the EOS instrument teams, wind fields for the transport studies of the stratospheric and tropospheric chemistry communities, and climate-quality analyses to support studies of the hydrological cycle through MERRA. The GEOS-5 atmospheric model has been approved for open source release and is available from: http://opensource.gsfc.nasa.gov/projects/GEOS-5/GEOS-5.php

Atmospheric Reanalyses-Recent Progress and Prospects for the Future. A Report from a Technical Workshop, April 2010

In April 2010, developers representing each of the major reanalysis centers met at Goddard Space Flight Center to discuss technical issues - system advances and lessons learned - associated with recent and ongoing atmospheric reanalyses and plans for the future. The meeting included overviews of each center s development efforts, a discussion of the issues in observations, models and data assimilation, and, finally, identification of priorities for future directions and potential areas of collaboration. This report summarizes the deliberations and recommendations from the meeting as well as some advances since the workshop

Evaluation of Precipitation Detection over Various Surfaces from Passive Microwave Imagers and Sounders

During the middle part of this decade a wide variety of passive microwave imagers and sounders will be unified in the Global Precipitation Measurement (GPM) mission to provide a common basis for frequent (3 hr), global precipitation monitoring. The ability of these sensors to detect precipitation by discerning it from non-precipitating background depends upon the channels available and characteristics of the surface and atmosphere. This study quantifies the minimum detectable precipitation rate and fraction of precipitation detected for four representative instruments (TMI, GMI, AMSU-A, and AMSU-B) that will be part of the GPM constellation. Observations for these instruments were constructed from equivalent channels on the SSMIS instrument on DMSP satellites F16 and F17 and matched to precipitation data from NOAA's National Mosaic and QPE (NMQ) during 2009 over the continuous United States. A variational optimal estimation retrieval of non-precipitation surface and atmosphere parameters was used to determine the consistency between the observed brightness temperatures and these parameters, with high cost function values shown to be related to precipitation. The minimum detectable precipitation rate, defined as the lowest rate for which probability of detection exceeds 50%, and the detected fraction of precipitation, are reported for each sensor, surface type (ocean, coast, bare land, snow cover) and precipitation type (rain, mix, snow). The best sensors over ocean and bare land were GMI (0.22 mm/hr minimum threshold and 90% of precipitation detected) and AMSU (0.26 mm/hr minimum threshold and 81% of precipitation detected), respectively. Over coasts (0.74 mm/hr threshold and 12% detected) and snow-covered surfaces (0.44 mm/hr threshold and 23% detected), AMSU again performed best but with much lower detection skill, whereas TMI had no skill over these surfaces. The sounders (particularly over water) benefited from the use of re-analysis data (vs. climatology) to set the a-priori atmospheric state and all instruments benefit from the use of a conditional snow cover emissivity database over land. It is recommended that real-time sources of these data be used in the operational GPM precipitation algorithms

Global Water Vapor Estimates from Measurements from Active GPS RO Sensors and Passive Infrared and Microwave Sounders

Water vapor plays an important role in both climate change processes and atmospheric chemistry and photochemistry. Global water vapor vertical profile can be derived from satellite infrared and microwave sounders. However, no single remote sensing technique is capable of completely fulfilling the needs for numerical weather prediction, chemistry, and climate studies in terms of vertical resolution, spatial and temporal coverage, and accuracy. In addition to the passive infrared and microwave sounder observations, the active global positioning system (GPS) radio occultation (RO) technique can also provide all-weather temperature and moisture profiles. In this chapter, we describe the current developments of global water vapor vertical profile and total precipitable water derived from active GPS RO measurements. In addition, we also demonstrate the potential improvement of global water vapor estimates using combined active GPS RO and passive IR/MW particularly from Atmospheric InfraRed Sounder (AIRS) and Advanced Technology Microwave Sounder (ATMS) measurements. Results show that because RO data are very sensitive to water vapor variation in the moisture rich troposphere, the RO data are able to provide extra water vapor information for the combined AIRS/ATMS and RO retrievals in the lower troposphere

Some challenges of middle atmosphere data assimilation

The assimilation of measurements from the stratosphere and mesosphere is becoming increasingly common as the lids of weather prediction and climate models rise into the mesosphere and thermosphere. However, the dynamics of the middle atmosphere pose specific challenges to the assimilation of measurements from this region. Forecast-error variances can be very large in the mesosphere and this can render assimilation schemes very sensitive to the details of the specification of forecast error correlations. An example is shown where observations in the stratosphere are able to produce increments in the mesosphere. Such sensitivity of the assimilation scheme to misspecification of covariances can also amplify any existing biases in measurements or forecasts. Since both models and measurements of the middle atmosphere are known to have biases, the separation of these sources of bias remains a issue. Finally, well-known deficiencies of assimilation schemes, such as the production of imbalanced states or the assumption of zero bias, are proposed explanations for the inaccurate transport resulting from assimilated winds. The inability of assimilated winds to accurately transport constituents in the middle atmosphere remains a fundamental issue limiting the use of assimilated products for applications involving longer time-scales