Regional assimilation experiments of clear-sky Atmospheric Infrared Sounder (AIRS) radiances were performed using the gridpoint statistical interpolation three-dimensional variational assimilation system coupled to the weather research and forecasting model. The data assimilation system and forecast model used in this study are separate community models; it cannot be assumed that the coupled systems work optimally. Tuning was performed on the data assimilation system and forecast model. Components tuned included the background error covariance matrix, the satellite radiance bias correction, the quality control procedures for AIRS radiances, the forecast model resolution, and the infrared channel selection. Assimilation metrics and diagnostics from the assimilation system were used to identify problems when combining separate systems. Forecasts initiated from analyses after assimilation were verified with model analyses, rawinsondes, nonassimilated satellite radiances, and 24 h–accumulated precipitation. Assimilation of clear sky AIRS radiances showed the largest improvement in temperature and radiance brightness temperature bias when compared with rawinsondes and satellite observations, respectively. Precipitation skill scores displayed minor changes with AIRS radiance assimilation. The 00 and 12 coordinated universal time (UTC) forecasts were typically of better quality than the 06 and 18 UTC forecasts, possibly due to the amount of AIRS data available for each assimilation cycle.
ACE is a proposed Tier 2 NASA Decadal Survey mission that will focus on clouds, aerosols, and precipitation as well as
ocean ecosystems. The primary objective of the clouds component of this mission is to advance our ability to predict
changes to the Earth’s hydrological cycle and energy balance in response to climate forcings by generating observational
constraints on future science questions, especially those associated with the effects of aerosol on clouds and
precipitation. ACE will continue and extend the measurement heritage that began with the A-Train and that will continue
through Earthcare. ACE planning efforts have identified several data streams that can contribute significantly to
characterizing the properties of clouds and precipitation and the physical processes that force these properties. These
include dual frequency Doppler radar, high spectral resolution lidar, polarimetric visible imagers, passive microwave and
submillimeter wave radiometry. While all these data streams are technologically feasible, their total cost is substantial
and likely prohibitive. It is, therefore, necessary to critically evaluate their contributions to the ACE science goals. We
have begun developing algorithms to explore this trade space. Specifically, we will describe our early exploratory
algorithms that take as input the set of potential ACE-like data streams and evaluate critically to what extent each data
stream influences the error in a specific cloud quantity retrieval.
The instantaneous daytime geographical distribution and radiative effects of high thin clouds (optical thickness < 5) are investigated on the basis of the CloudSat Cloud Profiling Radar (CPR) radiative flux and cloud classification products. The regional features of the fraction and radiative effects of high thin clouds are associated with ITCZ, SPCZ and mid-latitude storm track regions. High thin clouds have positive net cloud-induced radiative effect (CRE) at the top of the atmosphere (TOA) and negative net CRE at the bottom of the atmosphere (BOA). The magnitudes of TOA and BOA CREs depend on cloud optical thickness, cloud fraction and geographical location. The magnitude of the net CRE of high thin clouds increases at both TOA and BOA as cloud optical thickness increases. Net CRE at both TOA and BOA contributes to a positive net CRE in-atmosphere and warms the atmosphere regardless of cloud fraction. The global annual mean of the net CRE multiplied by cloud fraction is 0.49 W/m2 at TOA, -0.54 W/m2 at BOA and 1.03 W/m2 in-atmosphere. The most radiatively effective cloud optical thickness of a high thin cloud is between 1-2 for the TOA and in-atmosphere CREs or 3-4 for the BOA CRE.
The Submillimeter-wave and Infrared Ice Cloud Experiment (SIRICE) concept would provide global measurements of ice water path (IWP - the vertically integrated mass of ice particles per unit area), and weighted mean mass particle diameter (Dme). The SIRICE payload consists of two instruments, the Sub-millimeter/Millimeter (SM4) Radiometer, and the Infrared Cloud Ice Radiometer (IRCIR). IRCIR is a compact, low-cost, multi-spectral, wide field of view pushbroom infrared imaging radiometer. IRCIR will employ four IR sensor assemblies to produce 90° cross-track (contiguous along-track) coverage in three spectral bands with a spatial resolution of 0.6 km at nadir. Each IR sensor assembly consists of an uncooled microbolometer focal plane array (FPA), associated sensor core electronics, a stripe filter fixed at the FPA, and an IR lens assembly. A single scene mirror is used to provide two Earth view angles, as well as calibration views of space and the on-board calibration blackbody. The two Earth view angles will be used for stereo cloud height retrievals.
In 2006, we began a three-year project funded by the NASA Integrated Decisions Support program to develop a three-dimensional air quality system (3D-AQS). The focus of 3D-AQS is on the integration of aerosol-related NASA Earth Science Data into key air quality decision support systems used for air quality management, forecasting, and public health tracking. These will include the U.S. Environmental Protection Agency (EPA)'s Air Quality System/AirQuest and AIRNow, Infusing satellite Data into Environmental Applications (IDEA) product, U.S. Air Quality weblog (Smog Blog) and the Regional East Atmospheric Lidar Mesonet (REALM). The project will result in greater accessibility of satellite and lidar datasets that, when used in conjunction with the ground-based particulate matter monitors, will enable monitoring across horizontal and vertical dimensions. Monitoring in multiple dimensions will enhance the air quality community's ability to monitor and forecast the geospatial extent and transboundary transport of air pollutants, particularly fine particulate matter. This paper describes the concept of this multisensor system and gives current examples of the types of products that will result from it.
A new technique called the local region of influence (LROI) scheme for supervised cloud classification of the Moderate Resolution Imaging Spectroradiometer (MODIS) is proposed. The classification of each observation is performed within the LROI, where the center of each class is calculated as a weighted average of its training class members with respect to each new observation. The probability of each class is assigned to each observation. The proposed LROI scheme is applied to the MODIS radiances observed from the scenes of clear skies, ice clouds, or water clouds. The classification results are compared with those from the maximum likelihood (ML) classification method, the multicategory support vector machine (MSVM) and the operational MODIS cloud mask algorithm. The lowest misclassification error rates show the advantage of the LROI scheme.
This paper conducts a preliminary assessment of the cloud detection capability of the Japanese Global Imager (GLI). Cloud detection results from the satellite borne instrument are compared to other satellite, aircraft and ground-based observations. The performance is similar to that of the MODIS results.
Significant improvements have been made to the MODIS cloud mask (MOD35) in preparation for Collection 5 reprocessing and forward stream data production. Most of the modifications are realized for nighttime scenes where polar and oceanic regions will see marked improvement. For polar night scenes, two new spectral tests using the 7.2 μm water vapor absorption band have been added as well as updates to the 3.9-12 μm and 11-12 μm cloud tests. More non-MODIS ancillary data has been added for nighttime processing. Land and sea surface temperature maps provide crucial information for middle and low-level cloud detection and lessen dependence on ocean variability tests. Sun-glint areas are also improved by use of sea surface temperatures to aid in resolving observations with conflicting cloud vs. clear-sky signals, where visible and NIR reflectances are high, but infrared brightness temperatures are relatively warm. Details and examples of new and modified cloud tests are shown and various methods employed to evaluate the new cloud mask results. Day vs. night sea surface temperatures derived from MODIS radiances and using only the MODIS cloud mask for cloud screening are contrasted. Frequencies of cloud from sun-glint regions will be shown as a function of sun-glint angle to gain a sense of cloud mask quality in those regions.
The 36 channel Moderate Resolution Imaging Spectroradiometer (MODIS) offers the opportunity for multispectral approaches to cloud detection. The MODIS cloud mask developed at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) uses several cloud detection tests to indicate a level of confidence that the MODIS is observing clear skies. The MODIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. The updated cloud mask has many improvements, such as improved cloud/surface discrimination over desert regions, sun glint processing and thin cirrus detection. For non-snow-covered land areas, a clear sky confidence of 0.96 (probably clear) will be assigned if thresholds are met for three tests: 3.9-11 μm and 3.75-3.9 μm brightness temperature differences and a 1.24/0.55 μm reflectance ratio test. Values of these must be <15K, <11K and >2.0, respectively. A change has been made to the NIR (band 2) reflectance test for sun glint processing. The updated method is to calculate a cloud threshold as a linear function of sun-glint angle in three separate ranges. A new clear-sky restoral test was added where the ratio of band 17/18 reflectance is utilized to discriminate between low clouds and water surfaces. The thin cirrus thresholds using corrected band 26 (1.38 μm) reflectances were also modified.
Development in the mid 80s of the High-resolution Interferometer Sounder (HIS) for the high altitude NASA ER2 aircraft demonstrated the capability for advanced atmospheric temperature and water vapor sounding and set the stage for new satellite instruments that are now becoming a reality [AIRS (2002), CrIS (2006), IASI (2006), GIFTS (2005/6)]. Follow-on developments at the University of Wisconsin-Madison that employ interferometry for a wide range of Earth observations include the ground-based Atmospheric Emitted Radiance Interferometer (AERI) and the Scanning HIS aircraft instrument (S-HIS). The AERI was developed for the US DOE Atmospheric Radiation Measurement (ARM) Program, primarily to provide highly accurate radiance spectra for improving radiative transfer models. The continuously operating AERI soon demonstrated valuable new capabilities for sensing the rapidly changing state of the boundary layer and properties of the surface and clouds. The S-HIS is a smaller version of the original HIS that uses cross-track scanning to enhance spatial coverage. S-HIS and its close cousin, the NPOESS Airborne Sounder Testbed (NAST) operated by NASA Langley, are being used for satellite instrument validation and for atmospheric research. The calibration and noise performance of these and future satellite instruments is key to optimizing their remote sensing products. Recently developed techniques for improving effective radiometric performance by removing noise in post-processing is a primary subject of this paper.
In this study we compare different approaches to retrieve Cloud Top Height (CTH), Cloud Effective Emissivity (CEE), and the Cloud Particle Size (CPS) from aircraft high-spectral resolution infrared measurements. Two independent methods are used to infer CTH. One approach is based on a high spectral resolution version of the CO2 Slicing algorithm characterized by a statistically based selection of the optimal channel pairs. Another approach the Minimum Local Emissivity Variance algorithm (MLEV) takes advantage of high-resolution observations in the 8-12 micron region to simultaneously derive CTH and CEE. Once CTH has been retrieved a third method, based on the comparison between simulated and observed radiances, is used to infer CPS and CEE. Simulated radiances are computed for 18 microwindows between 8.5 and 12 microns. The cirrus scattering calculations are based on three-dimensional randomly oriented ice columns assuming six different particle size distributions. Multiple scattering calculations are performed for 26 different cloud optical thicknesses (COT) between 0 and 20. The simulated radiances are then compared to the observed radiances to infer COT and CPS for each spectral measurement. We applied these approaches to High-resolution Interferometer Sounder (HIS), National Polar-Orbiting Operational Environmental Satellite System Airborne Sounder Testbed-Interferometer (NAST-I) and Scanning-HIS (S-HIS) data. The preliminary results, consistent between the different algorithms, suggest that the high spectral resolution measurements improve the accuracy of the cloud property retrievals.
During the conference on 'passive remote sensing of clouds and the atmosphere III' in 1995, a comparison of cloud amounts derived from two retrieval schemes, APOLLO and CHAPS, was presented. It showed good results over ocean but not so good ones over land surfaces. Reasons for the discrepancy have been suggested. Meanwhile, a modified version of the APOLLO scheme has been developed and compared to a one year data set of synop data. They agree quite well. This modified version of APOLLO is used for a new comparison with CHAPS. Using the same data set, the agreement is very god. The remaining difference is probably due to insufficient cloud detection with CHAPS.
This paper presents a quantitative comparison oftwo cloud detection techniques using satellite
observations. The AVHRR (Advanced Very High Resolution Radiometer) Processing scheme
Over cLouds Land and Ocean (APOLLO) makes use of five spectral channels with a spatial
resolution of I .I km. The Collocated BIRS/2 and AVHRR ProductS (CHAPS) operates with
more spectral channels but a lower spatial resolution. To reference the satellite derived cloud
amounts, APOLLO results are compared with surface observations of cloud amount. The
APOLLO cloud amount and surface observations of cloud cover are generally within over
vegetated surfaces.
Over oceans, the agreement in total cloud cover between the two satellite techniques is very
good (r=O.92). Application of a dependent sample i-test to the two cloud amount data sets
indicates that there is a greater than 99.9% probability that the two samples were drawn from the
same population. This demonstrates that the subsampling of AVHRR pixels in the CHAPS
processing is appropriate for deriving cloud amounts over a 2.5degree oceanic region. For such
a region there is a tendency for CHAPS to derive higher cloud amounts than APOLLO. This is
attributed to differences in clear-sky radiance thresholds derived from the CHAPS spatial
variability test.
Over land, the derived cloud amount products from the two methods are considerably different.
The CHAPS product is an effective cloud amount defined for each HIRS field ofview which is
the product of cloud fraction and cloud emissivity rather than a simple areal percentage. Also,
the HIRS/2 footprint size (17 km at nadir) is much larger than that of the AVHRR. There is a
good correlation of the two cloud products (r=O.82); however, a t-test indicates the two
techniques are deriving fundamentally different parameters. This is consistent with the above
differences. Recommendations for improving the two cloud retrieval techniques are suggested.
Several methods exist for determining the presence of aerosol, and its optical thickness, from satellite visible and infrared observations. This paper suggests a new approach, physically based on the spectral variation of the index of refraction in the 8 - 12 micrometers region. The tri- spectral technique makes use of observations at wavelengths of 8, 11, and 12 micrometers . Observations from the AVHRR and HIRS/2 instruments demonstrate the potential of the technique.
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