Cgms-41 nasa-wp-06 11 June 2013



Download 94.56 Kb.
Date conversion20.10.2016
Size94.56 Kb.

CGMS-41 NASA-WP-06lgo_tpy_cgmslogo_bw_medium

11 June 2013





Prepared by NASA

Agenda Item: WGII/4

Discussed in Working Group II


Atmospheric Sounding Product Development and Cal/Val Activities at NASA using AIRS/AMSU on Aqua, CrIMSS on SNPP, and NAST-I on NASA High-altitude Aircraft Platforms

T. Pagano, E. Fetzer, B. Lambrigtsen, J. Teixeira, Steve Friedman

NASA Jet Propulsion Laboratory, Pasadena, CA
A. Larar, Daniel Zhou, Xu Liu
NASA Langley Research Center, Hampton, VA
Joel Susskind

NASA Goddard Spaceflight Center, Greenbelt, MD





Executive summary
Atmospheric sounding at NASA is a broad based effort to measure, characterize and understand the Earth’s atmosphere from ground, airborne and spaceborne platforms. This paper covers recent activities at NASA’s JPL, GSFC and LaRC centers in support of the AIRS, CrIS, IASI and NAST-I instruments. The AIRS and AMSU instruments on the EOS Aqua Spacecraft continue to operate well, however loss of channels has been seen. In AIRS, the impact is minimal, and over 50 channels were recovered using redundant detectors. AMSU loss of channel 5 severely impacted the AIRS/AMSU Level 2 Version 5 products, but the Version 6 mitigates this and other problems and was released in February of 2013 at the GES/DISC. The NASA teams have demonstrated Level 2 retrievals from the CrIS/ATMS (CrIMSS) instruments on the Suomi NPP satellite and has concluded the instrument is of high value to the scientific community. Comparisons of AIRS, CrIS and IASI have shown excellent radiometric agreement under normal conditions but differences do exist that are being carefully examined. The NAST-I aircraft sounder continues to provide excellent validation of the spaceborne sounders. New retrieval methodologies are being developed and utilized to improve computational speed, accuracy and error estimation. These new retrievals run on all three IR sounder instruments and will form the basis for a next generation retrieval for Version 7. Scientific interest in the sounding instruments remains high with over 100 peer reviewed publications released in 2012 using AIRS data and numerous more with the other sounder instruments.


Atmospheric Sounding Product Development and Cal/Val Activities at NASA using AIRS/AMSU on Aqua, CrIMSS on SNPP, and NAST-I on NASA High-altitude Aircraft Platforms
  1. Introduction

The Atmospheric Infrared Sounder (AIRS) and companion instrument, the Advanced Microwave Sounding Unit (AMSU) were launched on May 4, 2002 on the NASA Earth Observing System Aqua spacecraft and managed by NASA JPL. They are facility instruments designed to support measurements of atmospheric temperature, water vapor and a wide range of atmospheric constituents in support of weather forecasting and scientific research in climate and atmospheric chemistry. The AIRS measures the hyperspectral infrared spectrum of the atmosphere in 2378 channels from 3.7-15.4 microns with 13.5 km spatial resolution and nearly twice global daily coverage. The AMSU is a microwave radiometer with 15 channels ranging from 23.8 GHz to 89 GHz. National Weather Prediction (NWP) centers worldwide assimilate the AIRS and AMSU data with these instruments providing among the highest forecast improvement of any sensors. The spectra are also used to retrieve cloud cleared radiances, atmospheric temperature and water vapor profiles, and trace gas constituents with high accuracy and product resolutions approaching 50km per retrieval. The Cross-track Infrared Sounder (CrIS) on the Suomi National Polar-orbiting Partnership (NPP) has similar spectral range with 1305 spectral channels and similar spatial resolution as AIRS. The Advanced Technology Microwave Sounder (ATMS) also on Suomi NPP has 22 channels ranging from 23 GHz to 183 GHz and spatial resolution ranging from 15.8 to 75 km. In addition to the CNES Infrared Atmospheric Sounding Interferometer (IASI) on the European METOP spacecraft, these instruments form the basis for the satellite component of atmospheric sounding at NASA.


In addition to the satellite instruments, the airborne NAST-I is the NASA / National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed-Interferometer which LaRC maintains and deploys internationally, in support of satellite measurement system (sensor, algorithm, direct and derived data product) calibration / validation (cal/val) activities and NASA Earth system science advancement. NAST-I serves as an ideal validation sensor since it measures the same level-1 quantity as many sensors it helps to validate (i.e. infrared spectral radiance), and does so at higher spectral and spatial resolutions.
This working paper is organized with a report from the AIRS Project at NASA JPL and GSFC, followed by a report from the Atmospheric Sounding team at NASA LaRC. Finally, science highlights from the peer reviewed literature that involved use of AIRS data is highlighted at the end of this document. Further information can be found at http://airs.jpl.nasa.gov.
  1. NASA Atmospheric Infrared Sounder (AIRS) Project (JPL/GSFC)


The AIRS Project carries out instrument maintenance, operations, and calibration for AIRS and AMSU instruments, algorithm integration and testing for the AIRS/AMSU geophysical and gridded products, and validation and demonstration that the data products are suitable for research applications. The project provides user documentation and support, education and public outreach, and science team coordination (meetings, etc.). Data production, archive, and distribution are performed by the NASA/GSFC Goddard Earth Sciences (GES) Data and Information Services Center (DISC), NOAA NESDIS, and UW for the direct broadcast communities.

2.1 Instrument Operations

The AIRS instruments flies on the NASA Aqua spacecraft, managed and operated by NASA GSFC. The spacecraft is in excellent health and anticipated to continue operating until the fuel is exhausted in the 2022 timeframe.


AIRS remains in excellent health and continues to perform extremely well. Several minor anomalies have occurred over the course of the mission, the most recent occurring February 8, 2013, when a single-event upset (SEU) caused the instrument fault management system to shut down the scanner. After examination of instrument telemetry before and after the event indicated that AIRS was in good health, the scanner was simply restarted and AIRS was returned to normal operating mode on February 9, 2013.
AIRS engineering telemetry includes measurements of several hundred voltages and currents related to instrument health. For most of the parameters measured, no long-term trends exist. A few parameters are changing, but the rates of change are so low that danger levels are not expected to be reached for many years. The AIRS cooler active drive levels are important examples, because they are critical to the health of the instrument. For the past two years, the Cooler A and Cooler B drive levels have been increasing at ~0.1 % per year. At present rates, neither cooler would reach its yellow limit for over 50 years. AIRS can operate with just one cooler if necessary, but two are being used because lower drive levels result in longer expected lifetimes.
The AIRS operations team carefully monitors the health of the 2378 IR channels, of which 2104 channels (wavelengths 3.75 µm to 13.7 µm) are photovoltaic (PV). The 274 longest wavelength channels are photoconductive (PC). Over the nearly 11 years since launch, about 180 PV channels have degraded by becoming noisier than they were at launch. The noise changes are due to a buildup of radiation, either slowly over time or, in most cases, due to radiation hits in the South Atlantic Anomaly or in the polar horns of the Van Allen belt. (PC channels are highly resistant to radiation damage.) Each AIRS PV channel consists of two detectors whose data are combined (through a weighted average) on board. The weights are commandable, and when one of a channel’s detectors suffers a radiation hit but the other does not, the channel can be restored to near at-launch quality by changing the weights. A revised weight table uploaded to AIRS on January 21, 2012 restored about 90 channels to in-spec performance.
Additional recent activities included execution of a standard calibration sequence during the Aqua lunar roll maneuver to enable AIRS to view the moon. The resulting data will be of value in the long-term calibration of AIRS. Given the excellent condition of the AIRS hardware and the availability of the AIRS design and development database, AIRS is expected to continue to perform well for the life of the spacecraft (through 2022).

2.2 Instrument Calibration


AIRS radiances have demonstrated stability and accuracy suitable for a wide range of weather and climate studies, and they are now assimilated operationally at Numerical Weather Prediction (NWP_ centers worldwide for weather forecasting. AIRS radiances are routinely used to intercompare and calibrate with GOES, MODIS, CrIS, IASI, and the High Resolution Infrared Sounder (HIRS). Recent studies by the AIRS Team show agreement in zonal PDF’s of better than 50 mK between the AIRS, CrIS and IASI instruments.

None of the AIRS channels used by the NWP centers have degraded. However, small spectral and radiometric shifts over time, if left uncorrected, increase the difficulty of monitoring climate trends using AIRS radiances and may cause long-term trend artifacts in the geophysical products (Level 2). Tables have been developed for upload to the AIRS instrument to replace bad or noisy detectors with their healthy redundant detectors. In addition, a tool has been developed to de-noise (and flag appropriately) all noisy channels and to fill the twelve small gaps that exist in the spectra using Principal Component Reconstruction (Level 1C). This step will greatly facilitate the use of AIRS Level 1B data for the calibration of vintage GOES, HIRS, and AVHRR thermal infrared channels. Additionally, the tool will resample as determined by members of the AIRS science team frequencies to a common grid to better than 0.2 ppm for all observations. Lastly, the team continues to improve radiometric accuracy of the AIRS instrument through more thorough and refined analysis of the pre-flight and in-flight calibration data. The recent improvements and other QC related improvements will be implemented in Version 7.


2.3 AIRS/AMSU Geophysical Product Development and Validation




Table 1. AIRS Data Products and Validation Status


A description of the AIRS/AMSU geophysical data products and their validation status is given in Table 1. The products consist of calibrated radiances from the instruments, and numerous atmospheric and surface geophysical variables. The scale of resolution is approximately 45 km at nadir for most products except those in L1B and cloud top height which are 13.5 km for AIRS and 45 km for AMSU. Level 3 daily, 8 day and monthly gridded products are also available. An example monthly map of carbon dioxide is shown in Figure 1.
Algorithms for all products are provided by the AIRS science team, and integrated at NASA JPL. Products are validated by NASA and the scientific community against ground, airborne, balloon and other in-situ observations obtained from operational systems and research experiments. The AIRS project at JPL performs configuration management, extensive functional testing of the changes, and packaging and delivery to the operational production centers (NOAA, GSFC, and direct broadcast). A considerable amount of data product generation is also required at the AIRS facility to provide the matchup and analysis data necessary for testing and validation.
The AIRS/AMSU Version 5 Level 2 data products have provided high value to the scientific community since their release in July 2007. However, progressive degradation of AMSU channel 5 performance resulted in a significant degradation of yield in 2012. AIRSOnly Version 5 data products were available to mitigate this loss, but with slightly reduced performance in cloudy areas.

c:\users\tpagano\documents\my projects\airs\imagery&animations\co2\pagano\airs-co2midtrop-april2013.jpg

Figure 1. AIRS mid-tropospheric carbon dioxide for April 2013. Over 10 years of data are now archived.
Numerous improvements to the Level 2 data products have been successfully implemented in the AIRS/AMSU Version 6, which was completed in October 2012, and is now in production at the GES DISC. These improvements include less sensitivity to loss of AMSU channels, removal of an apparent 50-100 mK/yr bias drift in the mid-tropospheric temperature product, improved yield and sensitivity of temperature and water vapor near the surface, improvements to surface emissivity, and the inclusion of a radiative transfer algorithm (RTA) to accommodate scattering for cloud and dust retrievals. In addition, new cloud products, including cloud top thermodynamic phase, ice cloud optical thickness, and ice cloud effective diameter, are produced. The AIRS-Only Version 6 is also in production at the GES DISC. All Version 6 Level 2 and Level 3 data are scheduled to be released to the public on March 1, 2013, accompanied by a complete set of User Guides and a detailed Performance Verification Test Report. The remaining work on Version 5 and mostly Version 6 will involve a closer look at product accuracies trends, and yield through a comprehensive characterization and validation of the products and publishing results in the peer reviewed literature.
Support for Version 7 will be driven by the needs of the scientific and operational users and ROSES investigators. Necessary improvements for Version 7 include transitioning the cloud and AIRS-Only products into standard products, generating higher spatial resolution products, and incorporating data from other instruments (e.g., MODIS) and a new retrieval methodology providing improved error characterization. New versions, including and beyond Version 7, will be developed in close collaboration with the NASA NPP CrIS/ATMS science teams.
Also completed in 2012 were the AIRS Standard Level 3 gridded product (1-day, 8-day, and monthly) and a special gridded product consisting of temperature and humidity profiles designed for comparisons with CMIP5 models as part of the Observations for Model Intercomparison Projects (Obs4MIPs) (see http://esg-datanode.jpl.nasa.gov/esgf-web-fe/). Obs4MIPs is a key project for NASA that illustrates the relevance of remote sensing observations for the improvement of climate prediction, and AIRS has been at the forefront of this effort. The AIRS will continue to play an essential role in future Obs4MIPs activities. One key activity will be to support the development of an AIRS simulator to compute AIRS-like radiances from geophysical fields.

2.4 Operational Products and User Services

An essential new goal of NWP centers is the full utilization of cloud and water vapor information from IR sounders. The AIRS science team has received renewed interest from the Joint Center for Satellite Data Assimilation (JCSDA) and NOAA, among others, to address this goal. This will involve sharing experience with cloudy radiances, cloud properties, water vapor, and possible OSSE studies. The AIRS Project will continue to work with NOAA and the NASA Short-term Prediction Research and Transition Center (SPoRT) to develop a prototype application for use with the Advanced Weather Interactive Processing System-2 (AWIPS-2) terminals to display AIRS data. Work in 2012 demonstrated the skill and value of AIRS lower tropospheric water vapor compared to the current AWIPS-2 product. NASA will continue to host the atmospheric sounding community at annual NASA Sounding Science Team Meetings as well as focused workshops on IR sounding for weather, climate and atmospheric composition. NASA will continue to archive project documents, provide feature stories, user services, product multimedia, and a refereed publications database on the AIRS Project website: http://airs.jpl.nas.gov.


  1. Suomi NPP CrIMSS Science Team Activities

A NASA-funded S-NPP sounding science team is in the process of assessing the performance of the CrIMSS sounding suite and data products derived from it to support climate research. Following is a synopsis of findings to date, as reported in a recent interim report to NASA.


The performance of the Cross-track Infrared Sensor (CrIS) and Advanced Technology Microwave Sounder (ATMS) flying on Suomi-NPP, and the quality of the NOAA IDPS products derived from these sensors were evaluated. Both sensors are performing extremely well in orbit, clearly producing Sensor Data Records (SDRs) sufficiently accurate for NWP applications. Further, it is clear that the CrIS instrument is very well suited to continue, and in several ways to improve on, the high accuracy climate record from high resolution IR spectra begun by the AIRS instrument on the NASA EOS Aqua platform. This being said, a number of small liens on the CrIS and ATMS SDRs produced by the NOAA/IDPS will limit their utility for climate research, and further analysis of the SDRs (and their associated algorithms) is still needed for a full understanding of the climate quality of these records.
The retrievals from the Cross-track Infrared Microwave Sounding Suite (CrIMSS) Environmental Data Records (EDRs) as produced by the NOAA/IDPS is not on par with the SDRs. However the NOAA Unique CrIS ATMS Processing System (NUCAPS) system which is based on the AIRS/AMSU Version 5 processing system is producing good retrievals. A similar system at GSFC using the AIRS Version 6 production system on CrIMSS data is producing significantly better retrievals. Finally, retrievals have also been produced using the LaRC retrieval system. We will be generating monthly mean products from each system from CrIMSS and compare them to the AIRS/AMSU monthly means. The robustness of well calibrated hyperspectral infrared radiances for retrieval of atmospheric temperature and water vapor has been demonstrated. More work is required to evaluate the differences amongst these systems and with the trace gas products. Improved error estimation is a high priority for the next version.
  1. NAST-I and Ultra-spectral Infrared Remote Sensing at LaRC

Atmospheric sounding efforts led by researchers within the Science Directorate (SD) at NASA Langley Research Center (LaRC) contribute to the implementation and advancement of ultraspectral infrared remote sensing systems for weather, climate, chemistry, and radiation applications, and increase the societal benefits achievable from measurements by such systems on current and future satellite missions. While LaRC atmospheric sounding efforts are centered about the NAST-I airborne sensor regional deployments for satellite system validation (e.g., AIRS, IASI, and CrIS), subsequent analysis and radiative transfer and retrieval algorithm advancements have achieved global satellite and NWP program benefits internationally.


NAST-I is the NASA / National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed-Interferometer which LaRC maintains and deploys internationally, in support of satellite measurement system (sensor, algorithm, direct and derived data product) calibration / validation (cal/val) activities and NASA Earth system science advancement. NAST-I serves as an ideal validation sensor since it measures the same level-1 quantity as many sensors it helps to validate (i.e. infrared spectral radiance), and does so at higher spectral and spatial resolutions. LaRC also implements a set of internal algorithms for fast radiative transfer modeling and geophysical product retrievals to enable an independent assessment of derived level-2 products [Zhou et al., 2007a; 2009; Liu et al., 2007; 2009]. Methodologies have been validated with NAST-I data, implemented on AIRS and IASI, are now supporting the most recently-launched Suomi NPP CrIS (Cross-track Infrared Sounder) sensor. Table 2 summarizes NAST-I data products and their relevance toward field experimental campaign validation and science objectives. Earlier field campaign comparisons have shown IASI and AIRS spectral radiances both matching coincident NAST-I observations to within ~ 0.1 K (band-averaged). NAST-I can also serve as a calibration reference to compare other measurements indirectly, exploiting the fact that its accuracy stability is less uncertain than absolute accuracy itself; employing this approach for a JAIVEx flight has demonstrated longwave band differences between IASI and AIRS on the order of less than 0.05 K [Larar et al., 2010]. Such comparisons demonstrate the utility of airborne FTS sensors, such as the NAST-I, to serve as reference calibration standards for enabling inter-satellite cross-validation.
The most recent field experiment involving NAST-I and the first airborne cal / val campaign implemented for the Suomi NPP satellite was conducted 6-31 May, 2013 on the NASA ER-2 aircraft based out of Palmdale, CA; this was jointly sponsored by NASA SMD and the NOAA NESDIS JPSS program. Aircraft campaign flights focused on both Sensor Data Record (SDR) and Environmental Data Record (EDR) validation for the Cross-track Infrared Sounder (CrIS), Advanced Technology Microwave Sounder (ATMS), and Visible Infrared Imager Radiometer Suite (VIIRS) instruments aboard the SNPP satellite. In addition to NAST-I, the ER-2 payload included the NAST-M (MIT-LL), MASTER (NASA AMES), AVIRIS (NASA JPL), and S-HIS (UW-Madison) sensors. Some joint flights were also conducted with the UK Met Office BAe146 aircraft that was based nearby in Tucson, Arizona. Flight profiles included regions within Mexican airspace, i.e. over the Gulf of California to ensure capturing clear air over water during satellite overpass times, and over several instrumented sites including ground-based FTIR and radiosondes launches.

Table 2. NAST-I data product campaign and science relevance.



NAST-I data product

campaign relevance

Radiance

  • Infrared spectrum (3.5 – 15 m) with 0.25 cm-1 spectral resolution.

  • Infrared spectral radiative heating/cooling information during aircraft ascents and descents.

Science

  • Temperature and trace species information helpful to characterize the boundary layer and free troposphere; contributes to climate, air quality and biogeochemistry studies

  • Information on water vapor, ozone, cirrus clouds, and vertical profiles of infrared spectral radiance in the upper troposphere and lower stratosphere region beneficial for better understanding the radiative forcing of this region on climate system

  • Information on local meteorology, infrared heat budget, and trace gas evolution

Validation

  • Contributes toward radiance and geophysical product validation, e.g., AIRS, IASI, CrIS, MODIS, etc.

  • Inter-comparisons by spectral and spatial convolution to common resolution and coverage

  • High spatial resolution enables the effects of scene variability to be assessed

Future sensor studies

  • Direct and derived NAST-I products contribute toward instrument specification and retrieval studies for future sensors, e.g., missions defined within the National Research Council Earth Science Decadal Survey (NRC DS)

Atmospheric Thermodynamics

  • 3-d characterization of atmospheric state (temperature & moisture).

  • Profiles with vertical resolution of ~1-2 km for clear air or above clouds, vertical region dependent; horizontal resolution of < 130 m per km flight altitude

Atmospheric Composition

  • Profiles in clear air or above clouds with vertical resolution of 2 – 10 km, depending on altitude and atmospheric constituent

  • Tropospheric CO and O3 (PBL and free troposphere) from nominal flight altitude; other trace species (e.g., CO2, CH4, N2O, and SO2) also possible during platform ascents / descents.

Cloud Microphysical & Radiative Properties

  • Effective top height, temperature, & spatial extent

  • Effective particle size & optical depth

  • Spectral emissivity

Surface Properties

  • Temperature and spectral emissivity

4.1 Retrieval of geophysical products

LaRC employs a hybrid retrieval algorithm approach to simultaneously deal with clouds and trace species within the retrieval process [Zhou et al, 2007b]. The state of practice approach is to avoid clouds, through hole-hunting or cloud-clearing, but the LaRC approach is aimed at properly using cloudy ultrapectral data, and thus improving the accuracy and yield of derived geophysical products. The algorithm retrieves quantities to describe the thermodynamic state of atmosphere, surface and cloud properties, and trace species amounts from advanced sounder data. It was initially developed using NAST-I data and then implemented for both AIRS and IASI in support of validation of these sensors and their corresponding level-2 product retrieval algorithms; and, more recently, is being implemented for CrIS.


Figure 2 shows the evolution of NAST-I team retrievals for surface temperature and emissivity products [Zhou et al., 2011; 2013]. The retrieval was first developed for applying to regional airborne data from NAST-I field campaigns and has since evolved to global-scale application on satellite data. The top images in the figure demonstrate the ability to separate surface temperature and emissivity via a simultaneous retrieval applied to NAST-I data from the CLAMS field campaign. The plots in the middle section of the figure illustrate application to IASI data, utilizing measurements over the Sahara desert during 2007, and show radiance sensitivity to using the correct surface emissivity (i.e. via radiance residuals as one goes from unity to retrieved emissivity in the green to red curve, respectively). Finally, the bottom images in Figure 2 show week 1 global products for the 4-year weekly climatology of global land surface temperature and emissivity derived from IASI measurements obtained during June 2007 – June 2011. These types of global data products have been made available to the international community in support of research and operational programs. LaRC has more than a dozen international collaborators using these IASI global surface climatology data within their programs.

Figure 2. Retrieval of global surface temperature and emissivity: evolution from regional airborne NAST-I development to implementation on global scale for satellite products from IASI.
smdemis

4.2 Fast forward radiative transfer model

LaRC fast radiative transfer algorithm development is focused on enabling greater benefit to be achieved from ultraspectral remote sensing systems. The challenge being addressed is that modern ultraspectral sensors have high information content, but handling data volume is very problematic. Such instrument systems often have two orders of magnitude more spectral channels and increased dimensionality than traditional sensors. Due to computational expense, however, only a few hundred channels are typically used in retrievals and data assimilation which yields sub-optimal results.


The LaRC solution approach is to develop a super fast and accurate radiative transfer (RT) model (Principal Component Radiative Transfer Model, PCRTM), in an Empirical Orthogonal Function (EOF) domain [Liu et al., 2006, 2007; 2009], to enable using information from all channels by reducing channel dimensionality. This enables an operational processing alternative, and independent forward model, radiance, and geophysical product validation that is an order of magnitude faster than current channel-by-channel based RT models. This model approach has been validated using clear scenes from NAST-I, AIRS, and IASI, and the achieved accuracy compares favorably with international community standards (i.e., via the ITWG (International TOVS Working Group) Radiative Transfer Working Group radiative transfer algorithm inter-comparison). This model has also been implemented at LaRC within an independent set of physical retrieval algorithms using all channels for several sensors (i.e., NAST-I, AIRS, IASI, and CrIS).
smdpc1


Figure 3. IASI CO retrieval using PCRTM. a) comparison of IASI instrument measured and PCRTM calculated, and b) example CO retrieval global product.

Figure 3 shows an example implementation of PCRTM for a global trace gas CO retrieval from IASI. Figure 3-a) shows a comparison between IASI measured (OBS) and PCRTM calculated (CALC), wherein CO is excluded from the calculations in the middle plot but included within those for the bottom plot. Notice that the feature near 2020-2250 cm-1 is removed from the OBS-CALC curve when the CO profile is explicitly included in the radiative transfer algorithm (bottom plot) and, hence, that the CO signal in the IASI measurements is above the noise level and therefore the ability to retrieve this trace gas. Also of significance is that this figure shows the calculations with PCRTM are able to match the measurements at a level below the noise.


The motivation behind PCRTM is that line-by-line accuracy is desired at computational speeds much quicker in order to be feasible for radiative transfer calculations in operational processing environments. This approach is fast since principal components are used to reduce dimensionality in the spectral domain, but since the entire spectral extent is represented the fast model is able to keep high accuracy. While implementation for any advanced sensor is beneficial, the largest impact would be realized for large-format imaging spectrometers applications wherein thousands of spectral channels from thousands of spatial pixels must be handled.


4.3 Suomi NPP/JPSS CrIMSS operational algorithm

The LaRC NAST-I team is supporting efforts to mitigate risk for the CrIMSS (Cross-track Infrared Microwave Sensor Suite) operational algorithm being used for Suomi NPP and JPSS operational processing producing EDRs (Environmental Data Records) [Liu et al., 2010]. Activities have focused on porting and implementing the operational code onto LaRC computers, testing it using proxy and synthetic data and, most recently, implementing these algorithms on new CrIMSS flight data. Proxy data have been generated using data from the Aqua and Metop platforms. This has helped to identify areas of deficiencies in both forward and inverse models, testing robustness of the operational code, checking error handling capability of the OPS code, and EDR performance evaluations. LaRC activities are focusing on identifying issues, adding improvements, and assisting code implementation for operational processing. Figure 4 shows sample CrIMSS EDR algorithm products for 24 February 2012.



Figure 4: Retrieved 500 mb a) temperature (K) and b) water vapor (g/kg) from the CrIMSS algorithm using Suomi NPP data from February 24, 2012

In addition to working with the official operational algorithm being used for CrIMSS, LaRC is also contributing toward CrIMSS algorithm and product risk mitigation through the development and implementation of two other independent L2 retrieval algorithms for CrIS, including both channel- and PC-based approaches for single field-of-view (SFOV) IR retrievals for CrIS. These algorithms have been developed and are currently being implemented on the CrIS flight data.


4.4 NWP data assimilation impact studies

One of the major objectives of current and future weather satellite measurement programs is to help improve global NWP. Positive impact has already been shown with current AIRS and IASI radiance measurements from several NWP models at various forecast centers (e.g., UK Met Office, the GMAO, and the JCSDA). However, the studies with these models using satellite ultra-spectral sounding measurements still need huge efforts to fully use these satellite data. Since only a limited number of channel radiances are currently used in these models, the full potential of ultra-spectral measurements is not currently being realized. Dimensionality reduction could be achieved using a transformation such as PCRTM or, possibly, this could be achieved in the geophysical domain by usage of retrievals produced utilizing most of the channels as an alternative approach in NWP assimilation. The LaRC team is participating with NOAA on an NWP impact study using the Global Forecast System (GFS) NWP model. The focus of this effort is to evaluate the impact of improved surface emissivity assimilation on model forecast skill. By using LaRC-provided IASI IR land surface emissivity in the GSI data assimilation system, the GFS global forecast skill is improved in terms of anomaly correlations (ACs) for geopotential height, temperature, and wind over all vertical levels. A positive impact on GFS forecast has been achieved from 4 to 7 days.


  1. AIRS Science Highlights

More than 600 published articles have used AIRS data since launch. This section describes some recent studies grouped into general themes of Weather, Climate and Atmospheric Composition (including carbon dioxide), citing some of those articles.


5.1 Weather




macintosh hd:users:cparkins:desktop:y130131_forecasterrorfig_2-3-1_modified.png

Figure 5. ECMWF Forecast Error Contribution (FEC) for a variety of observational systems, showing AIRS as the single most important instrument in improving weather forecasts, second only to a suite of four AMSU-A units. FEC is an estimate of the forecast error due to the absence of a particular system, so that a larger FEC implies a more positive contribution. (Cardinali and Healy, 2012).
AIRS radiance data are assimilated routinely at virtually all operational weather prediction centers around the world. These centers use a service provided by the NOAA National Environmental Satellite, Data, and Information Service Center for Satellite Applications and Research, distributing AIRS radiance data worldwide with a latency of less than 3 hours.
The European Centre for Medium-Range Weather Forecasts (ECMWF) was the first center to adopt operational use of AIRS data and has found the impact on operational forecasts of assimilating AIRS and IASI data to be roughly comparable and second only to the collective impact of assimilating four AMSU units [Cardinali and Healy 2012] (See Figure 5). All major NWP centers in the U.S. have also shown notable improvements when assimilating AIRS data into their operational forecast systems [Baker et al. 2012]. AIRS is one of the highest-ranked contributors to global forecast skill [WMO 2012].
AIRS offers much potential for forecast improvement, as most models currently assimilate only clear-sky AIRS radiances. Assimilation of AIRS cloud-cleared geophysical products, significantly improve predictions of tropical rainfall intensity (compared to forecasts with only clear radiances) [Zhou et al. 2010]. Assimilating quality controlled AIRS temperatures in partly cloudy conditions gives substantially better 7-day forecasts of an extreme rainfall event over the Indus River Valley of Pakistan in 2010 versus using only clear-sky radiances [Reale et al., 2012]. Other regional studies assimilating AIRS retrievals show significant improvements in weather forecasts over Indian [Singh et al. 2011]; improved regional analyses and heavy-precipitation forecasts [Zavodsky et al. 2012]; and improved tropical cyclone predictions [Li et al. (2012); Miyoshi and Kunii 2012]. AIRS observations are also used to test model forecasts of cloud parameters [Garand et al. 2011]
Other studies examined weather processes with AIRS data near tropical cyclones and severe weather. One study finds that environmental relative humidity generally increases with tropical cyclone intensity and intensification rate in nine years of observations [Wu et al. 2012], and another examines the interactions of relative humidity and dust near Hurricane Isabel [Pan et al. 2011]. AIRS ozone retrievals can be improved near hurricanes, and additional information can be obtained in cloudier regions [Wang et al. 2012], and reanalysis can be improved near severe weather by the incorporation of additional AIRS information [Botes et al. 2012]. AIRS near-surface air temperatures during western North American winter snowstorms are more realistic than those in reanalysis of the National Centers for Environmental Prediction (NCEP) [Guan et al. 2012].
Over polar regions, AIRS data and model reanalysis characterize the structure of inversions in the Arctic atmospheric boundary layer, important in the Arctic surface energy budget [Pavelsky et al. (2011); Devasthale et al. (2011], and AIRS-derived relative humidity, air temperature, and surface temperature constrain the moisture flux from an Arctic polynya [Boisvert et al. 2012].
Several recent studies have used AIRS data to examine gravity waves, with results that include a better understanding of Andean mountain wave effects on regional and global circulation [Alexander and Teitelbaum 2011], a validation of the distribution of modeled gravity waves [Choi et al. 2012], the identification of previously unknown gravity wave sources [Eckermann and Wu 2012, Hoffmann et al. 2013], and the detection and characterization of shallow inertia-gravity waves [Gong et al. 2012]. A study integrating data from several sources finds a suggestion that AIRS stratospheric gravity waves are associated with ozone loss over Antarctica [Lambert et al., 2012].

5.2 Climate

The combined use of AIRS water vapor, winds from NASA’s Modern Era Retrospective Analysis for Research and Applications (MERRA), and estimates of surface evaporation and precipitation lead to closure of the atmospheric hydrological cycle both regionally and globally [Wong et al. 2011a, b]. The anomalies of global mean, and especially tropical mean, outgoing longwave radiation values derived from AIRS and CERES are strongly correlated with El Niño [Susskind et al. 2012].

Several recent studies use AIRS and AMSU data to obtain new information on clouds. These include a global climatology of bulk microphysical properties of semi-transparent cirrus clouds from AIRS data [Guignard et al. 2012]; the global distributions of clear-sky, heterogeneous and homogeneous clouds at the AIRS scale [Kahn et al. 2011a]; the radiance signature of deep convective clouds [Aumann et al. 2011]; and AIRS temperature and humidity atmospheric structure in the presence of deep clouds [Zelinka and Hartmann 2011].
A merged AIRS and Microwave Limb Sounder data set shows that tropical upper tropospheric water vapor distributions are different depending on whether the Quasi-Biennial Oscillation and the El Niño Southern Oscillation are in or out of phase, thereby suggesting a connection between stratospheric dynamics and tropospheric variability [Liang et al. 2011]. Small variations in AIRS temperature over the Niño 3.4 region of the Pacific Ocean are traced to changes in solar output during the declining phase of solar activity [Ruzmaikin and Aumann 2012].
AIRS temperature and humidity profiles are being used to derive large-scale stability parameters [Su et al. 2011]. AIRS observations of boundary layer structure and CloudSat observations of low-level clouds show the relationship between marine boundary layer clouds and lower tropospheric stability [Yue et al. 2011], and AIRS near-surface temperature perturbations are collocated with underlying ocean temperature fronts off South America and the east coasts of Asia and North America [Shimada and Minobe 2011]
AIRS data have been used to help develop and evaluate model parameterizations [Russo et al. 2011, Molod 2012] and to evaluate CMIP5 models [Jiang et al. 2012a, Tian et al. 2013]. Climate models systematically underestimate thermodynamic variance at small scales [Kahn et al. (2011)]. AIRS and CERES observations, together with climate model simulations, suggest that mid-tropospheric relative humidity (RH) is an observational constraint on climate sensitivity [Fasullo and Trenberth 2012]. The coherence between RH and albedo variations underscores the utility of RH in diagnosing clouds, which are the main reason for albedo variability and uncertainty in model-predicted climate sensitivity. This paper also shows that realistic models have the highest projected warming in doubled CO2 experiments, a result receiving considerable media attention when it first appeared.

5.3 Atmospheric Composition

AIRS CO2 during strong and weak monsoons years correlates well with the Tropical Biennial Oscillation index. A proposed mechanism of vertical transport in the Western Walker Cell yields greater CO2 concentrations during strong monsoon years [Wang et al. 2011a]. AIRS free tropospheric CO2 during El Niño and La Niña shows effects of a changing Walker Circulation, with a signal similar to --but about twice as large as-- that generated in the Model for Ozone and Related Chemical Tracers (MOZART) model [Jiang et al. 2012b]. The incorporation of data from AIRS and the Greenhouse Gases Observing Satellite (GOSAT) into a carbon cycle data assimilation system with the Local Ensemble Transform Kalman Filter (LETKF) helps constrain surface carbon fluxes, the greatest source of uncertainty in the carbon budget [Kang et al. 2012]. Principal component analysis and empirical mode decomposition applied to AIRS mid-tropospheric CO2 shows that the first mode amplitude matches the in situ interannual growth trend, while higher-order modes display a semiannual oscillation. Retrieved CO2 concentrations closely match isentropes at middle and high latitudes, in agreement with models [Ruzmaikin et al. 2012]


  1. Summary and Conclusions

Atmospheric sounding continues to be a high priority at NASA. The NASA Atmospheric Infrared Sounding (AIRS) instrument continues to operate successfully with full NASA support. With loss of over 100 channels since launch due to radiation exposure, the majority of the 2378 channels remain intact and provide sufficient information for continuity of the geophysical data products. Improvements in the Version 6 Product Generation System for AIRS have increased yield and accuracy considerably and removed a large bias trend in the temperature. The NAST-I instrument is in full operations and is currently being utilized to validate the CrIS instrument on Suomi NPP. The team continues to validate the data products and improve retrieval methodologies in preparation for a complete production system using the CrIS and ATMS instruments. The science interest in these instruments is still very high as indicated by the numerous science findings made over the last two years.


  1. References

Alexander, M. J., and H. Teitelbaum, 2011: Three dimensional properties of Andes mountain waves observed by satellite: A case study, J. Geophys. Re., 116, D23110, doi:10.1029/2011JD016151.


Aumann, H. H., S. G. DeSouza-Machado, and A. Behrangi, 2011: Deep convective clouds at the tropopause, Atmos. Chem. Phys., 11, 1167-1176, doi:10.5194/acp-11-1167-2011.
Baker, N., D. Merkova, R. Langland, R. Gelaro, P. M. Pauley, W. McCarthy, and L. Xu, 2012: The impact of satellite atmospheric motion vectors in the Navy and NASA global data assimilation systems, JCSDA Quarterly, 39, 1-2.
Boisvert, L. N., T. Markus, C. L. Parkinson, and T. Vihma, 2012: Moisture fluxes derived from EOS Aqua satellite data for the north water polynya over 2003– 2009, J. Geophys. Res., 117, D06119, doi:10.1029/2011JD016949.
Botes, D., J. R. Mecikalski, and G. J. Jedlovec, 2012: Atmospheric Infrared Sounder (AIRS) sounding evaluation and analysis of the pre-convective environment, J. Geophys. Res., 117, D09205, doi:10.1029/2011JD016996.
Cardinali, C., and S. Healy, 2012: Forecast sensitivity to observation error variance, ECMWF Newsletter, 133, 30-33.

Choi, H.-J., H.-Y. Chun, J. Gong, and D. L. Wu, 2012: Comparison of gravity wave temperature variances from ray-based spectral parameterization of convective gravity wave drag with AIRS observations, J. Geophys. Res., 117, D05115, doi:10.1029/2011JD016900.1.


Devasthale, A., J. Sedlar, and M. Tjernström, 2011: Characteristics of water-vapour inversions observed over the Arctic by Atmospheric Infrared Sounder (AIRS) and radiosondes, Atmos. Chem. Phys., 11, 9813-9823, doi:10.5194/acp-11-9813-2011.
Eckermann, S. D., and D. L. Wu, 2012: Satellite detection of orographic gravity-wave activity in the winter subtropical stratosphere over Australia and Africa, Geophys. Res. Lett., 39, L21807, doi:1029/ 2012GL053791.
Fasullo, J. T., and K. E. Trenberth, 2012: A less cloudy future: The role of subtropical subsidence in climate sensitivity, Science, 338, doi:10.1126/science.1227465.
Garand, L., O. Pancrati and S. Heilliette, 2011: Validation of forecast cloud parameters from multispectral AIRS radiances, Atmosphere-Ocean, 49 (2), 121-137.
Gong, J., D. L. Wu, and S. D. Eckermann, 2012: Gravity wave variances and propagation derived from AIRS radiances, Atmos. Chem. Phys., 12, 1701–1720.
Guan, B., D. E. Waliser, N. P. Molotch, E. J. Fetzer and P. J. Neiman, 2012: Does the Madden-Julian Oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada?, Mon. Wea. Rev., 140, 325-342, doi:10.1175/MWR-D-11-00087.1.
Guignard, A., C. J. Stubenrauch, A. J. Baran, and R. Armante, 2012: Bulk microphysical properties of semitransparent cirrus from AIRS: A six year global climatology and statistical analysis in synergy with geometrical profiling data from CloudSat-CALIPSO, Atmos. Chem. Phys., 12, 503-525.
Hoffmann, L., X. Xue, and M. J. Alexander, 2013: A global view of stratospheric gravity wave hotspots located with Atmospheric Infrared Sounder observations, J. Geophys. Res., doi:10.1029/2012JD018658.
Jiang, J. H., H. Su, C. Zhai, V. S. Perun, A. Del Genio, L. S. Nazarenko, L. J. Donner, L. Horowitz, C. Seman, J. Cole, A. Gettelman, M. A. Ringer, L. Rotstayn, S. Jeffrey, T. Wu, F. Brient, J.-L. Dufresne, H. Kawai, T. Koshiro, M. Watanabe, T. S. LÉcuyer, E. M. Volodin, T. Iversen, H. Drange, M. D. S. Mesquita, W. G. Read, J. W. Waters, B. Tian, J. Teixeira, and G. L. Stephens, 2012a: Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A Train” satellite observations, J. Geophys. Res., 117, D14105, doi:10.1029/2011JD017237.
Jiang, X., J. Wang, E. T. Olsen, M. Liang, T. S. Pagano, L. L. Chen, S. J. Licata, and Y. L. Yung, 2012b: Influence of El Nino on mid-tropospheric CO2 from Atmospheric Infrared Sounder and model, J. Atmos. Sci., 70, 223-230, doi:10.1175/JAS-D-11-0282.1.
Kahn, B. H., J. Teixeira, E. J. Fetzer, A. Gettelman, S. M. Hristova-Veleva, X. Huang, A. K. Kochanski, M. Köhler, S. K. Krueger, R. Wood, and M. Zhao, 2011: Temperature and water vapor variance scaling in global models: Comparisons to satellite and aircraft data, J. Atmos. Sci., 68, 2156–2168, doi:10.1175/2011JAS3737.1.
Kang, J.-S., E. Kalnay, T. Miyoshi, J. Liu, and I. Fung, 2012: Estimation of surface carbon fluxes with an advanced data assimilation methodology, J. Geophys. Res. Atmos., 117, D24101, doi:10.1029/2012JD018259.
Lambert, A., M. L. Santee, D. L. Wu, and J. H. Chae, 2012: A-train CALIOP and MLS observations of early winter Antarctic polar stratospheric clouds and nitric acid in 2008, Atmos. Chem. Phys., 12, 2899-2931, doi:10.5194/acp-12-2899-2012.
Li, J., J. Li, J. Zheng, and T. Schmit, 2012: Improving tropical cyclone forecasts with water vapor and temperature information from satellites, JCSDA Quarterly, 38, 1-2.
Liang, C. K., A. Eldering, A. Gettelman, B. Tian, S. Wong, E. J. Fetzer, and K. N. Liou, 2011: Record of tropical interannual variability of temperature and water vapor from a combined AIRS MLS data set, J. Geophys. Res., 116, D06103, doi:10.1029/2010JD014841.
Larar, A. M, W. L. Smith, D. K. Zhou, X. Liu, H. Revercomb, J. P. Taylor, S. M. Newman, and P. Schlüssel (2010), IASI spectral radiance validation inter-comparisons: case study assessment from the JAIVEx field campaign, Atmos. Chem. Phys., 10, 441–430, http://dx.doi.org/10.5194/acp-10-411-2010.
Liu, X., W. L. Smith, D. K. Zhou, and A. M. Larar (2006), A Principal Component-based Radiative Transfer Forward model (PCRTM) for hyperspectral sensors, part I: theoretical concept, Appl. Opt., 45, 201–209, http://dx.doi.org/10.1364/AO.45.000201.
Liu, X., D. K. Zhou, A. M. Larar, W. L. Smith, and S. A. Mango (2007), Case study of a principal-component-based radiative transfer forward model and retrieval algorithm using EAQUATE data, Q. J. R. Meteorol. Soc., 133, 243–256, http://dx.doi.org/10.1002/qj.156.
Liu, X., D. K. Zhou, A. M. Larar, W. L. Smith, P. Schlüssel, S. M. Newman, J. P. Taylor, and W. Wu (2009), Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels, Atmos. Chem. Phys., 9, 9121–9142, http://dx.doi.org/10.5194/acp-9-9121-2009.

Liu, X., S. Kizer, A. M. Larar, D. K. Zhou, W. L. Smith, C. Barnet, M. G. Divakarla, G. Guo, W. J. Blackwell, V. Leslie, L. Jairam, K. St. Germain, and R. J. Lynch (2010), Testing NPOESS CrIMSS EDR performance using proxy data from IASI and AMSU, Proc. SPIE 7857, 785705 (2010); doi:10.1117/12.869428.


Liu, X., D. K. Zhou, A. M. Larar, W. L. Smith, P. Schlüssel, S. M. Newman, J. P. Taylor, and W. Wu (2009), Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels, Atmos. Chem. Phys., 9, 9121–9142, http://dx.doi.org/10.5194/acp-9-9121-2009.
Miyoshi, T., and M. Kunii, 2012: Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction, Tellus Series A Dynamic Meteorology and Oceanography, 64, 18408.
Molod, A., 2012: Constraints on the profiles of total water PDF in AGCMs from AIRS and a high-resolution model, J. Clim., 25, 8341-8352.
Pan, W. Y., L. G. Wu, and C.-L. Shie, 2011: Influence of the Saharan Air Layer on Atlantic tropical cyclone formation during the period 1–12 September 2003, Adv. Atmos. Sci., 28 (1), 16–32, doi:10.1007/ s00376-010-9165-5.
Pavelsky, T., J. Boé, A. Hall, and E. J. Fetzer, 2011: Atmospheric inversion strength over polar oceans in winter regulated by sea ice, Clim. Dyn., 36, 945–955.
Reale, O., K. M. Lau, J. Susskind, and R. Rosenberg, 2012: AIRS impact on analysis and forecast of an extreme rainfall event (Indus River Valley, Pakistan, 2010) with a global data assimilation and forecast system, J. Geophys. Res., 117, D08103, doi:10.1029/2011JD017093.
Russo, M. R., V. Marécal, C. R. Hoyle, J. Arteta, C. Chemel, M. P. Chipperfield, O. Dessens, W. Feng, J. S. Hosking, P. J. Telford, O. Wild, X. Yang, and J. A. Pyle, 2011: Representation of tropical deep convection in atmospheric models – Part 1: Meteorology and comparison with satellite observations, Atmos. Chem. Phys., 11, 2765-2786, doi:10.5194/acp-11-2765-2011.
Ruzmaikin, A., and H. H. Aumann, 2012: Decadal variability of tropical Pacific temperature in relation to solar cycles, Advances in Space Research, 49 (3), 572-578.

Ruzmaikin, A., H. H. Aumann, and T. S. Pagano, 2012: Patterns of CO2 variability from global satellite data, J. Clim., 25, 6383–6393, doi:10.1175/JCLI-D-11-00223.1.


Shimada, T., and S. Minobe, 2011: Global analysis of the pressure adjustment mechanism over sea surface temperature fronts using AIRS/Aqua data, Geophys. Res. Lett., 38, L06704, doi:10.1029/2010GL046625.
Singh, R., C. M. Kishtawal, and P. K. Pal, 2011: Use of Atmospheric Infrared Sounder clear sky and cloud cleared radiances in the Weather Research and Forecasting 3DVAR assimilation system for mesoscale weather predictions over the Indian region, J. Geophys. Res., 116, D22116, doi:10.1029/2011JD016379.
Su, H., J. H. Jiang, J. Teixeira, A. Gettelman, X. Huang, G. Stephens, D. Vane, and V. S. Perun, 2011: Comparison of regime-sorted tropical cloud profiles observed by CloudSat with GEOS5 analyses and two general circulation model simulations, J. Geophys. Res., 116, D0910, doi:10.1029/2010JD014971.
Susskind, J., G. Molnar, L. Iredell, and N. G. Loeb, 2012: Interannual variability of outgoing longwave radiation as observed by AIRS and CERES, J. Geophys. Res., 117, D23107, doi:10.1029/2012JD017997.
Tian, B., E. J. Fetzer, B. H. Kahn, J. Teixeira, E. Manning, and T. Hearty, 2013: Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology, J. Geophys. Res. Atmos., 118 (1), 114-134, doi:10.1029/2012JD018607.
WMO (World Meteorological Organization), 2012: Proceedings of the 5th WMO Workshop on the Impacts of Various Observing Systems on Numerical Weather Prediction, WMO Tech. Rep. 01/2012, World Meteorological Organization, Geneva, Switzerland, 24 pp.
Wang, J., X. Jiang, M. T. Chahine, M.-C. Liang, E. T. Olsen, L. L. Chen, S. J. Licata, T. S. Pagano, and Y. L. Yung, 2011a: The influence of tropospheric biennial oscillation on mid-tropospheric CO2 , Geophys. Res. Lett., 38, L20805, doi:10.1029/2011GL049288.
Wang, L., M. Goldberg, X. Wu, C. Cao, R. A. Iacovazzi Jr., F. Yu, and Y. Li, 2011b: Consistency assessment of Atmospheric Infrared Sounder and Infrared Atmospheric Sounding Interferometer radiances: Double differences versus simultaneous nadir overpasses, J. Geophys. Res., 116, D11111, doi:10.1029/2010JD014988.
Wong, S., E. J. Fetzer, B. H. Kahn, B. Tian, B. Lambrigtsen, and H. Ye, 2011a: Closing the global water vapor budget with AIRS water vapor, MERRA reanalysis, TRMM and GPCP precipitation, and GSSTF surface evaporation, J. Climate, 24, 6307–6321.
Wong, S., E. J. Fetzer, B. Tian, and B. Lambrigtsen, 2011b: The apparent water vapor sinks and heat sources associated with the Intraseasonal Oscillation of the Indian summer monsoon, J. Clim., 24, 4466- 4479.
Wu, L., H. Su, R. G. Fovell, B. Wang, J. T. Shen, B. H. Kahn, S. M. Hristova-Veleva, B. H. Lambrigtsen, E. J. Fetzer, and J. H. Jiang, 2012: Relationship of environmental relative humidity with North Atlantic tropical cyclone intensity and intensification rate, Geophys. Res. Lett., 39, L20809, doi:10.1029/2012GL053546.

Yue, Q., B. H. Kahn, E. J. Fetzer, and J. Teixeira, 2011: Relationship between marine boundary layer clouds and lower tropospheric stability observed by AIRS, CloudSat, and CALIOP, J. Geophys. Res., 116, D18212, doi:10.1029/2011JD016136.


Zavodsky, B. T., S. Chou, and G. J. Jedlovec, 2012: Improved regional analyses and heavy precipitation forecasts with assimilation of Atmospheric Infrared Sounder retrieved thermodynamic profiles, IEEE Trans. Geosci. Remote Sens., 50 (11), 4243-4251.
Zelinka, M. D., and D. L. Hartmann, 2011: The observed sensitivity of high clouds to mean surface temperature anomalies in the tropics, J. Geophys. Res., 116 (D23), D23103.
Zhou, Y. P., K.-M. Lau, O. Reale, and R. Rosenberg, 2010: AIRS impact on precipitation analysis and forecast of tropical cyclones in a global data assimilation and forecast system, Geophys. Res. Lett., 37, L02806, doi:10.1029/2009GL041494.
Zhou, D. K., A. M. Larar, X. Liu, W. L. Smith, L. L. Strow, P. Yang, P. Schlüssel, and X. Calbet (2011), Global land surface emissivity retrieved from satellite ultraspectral IR measurements, IEEE Trans. Geosci. Remote Sens., 49, 1277–1290, http://dx.doi.org/10.1109/TGRS.2010.2051036.
Zhou, D. K., W. L. Smith, V. Cuomo, J. P. Taylor, C. D. Barnet, P. Di Girolamo, G. Pappalardo, A. M. Larar, X. Liu, S. M. Newman, C. Lee, and S. A. Mango (2007a), Retrieval validation during the European Aqua Thermodynamic Experiment, Q. J. R. Meteorol. Soc., 133, 203–215, http://dx.doi.org/10.1002/qj.181.
Zhou, D. K., W. L. Smith, Sr., X. Liu, A. M. Larar, S. A. Mango, and H.-L. Huang (2007b), Physically retrieving cloud and thermodynamic parameters from ultraspectral IR measurements, J. Atmos. Sci., 64, 969–982, http://dx.doi.org/10.1175/JAS3877.1.
Zhou, D. K., A. M. Larar, and X. Liu (2013), MetOp-A/IASI Observed Continental Thermal IR Emissivity Variations, IEEE JSTARS, 6, http://dx.doi.org/10.1109/JSTARS.2013.2238892.


The database is protected by copyright ©ininet.org 2016
send message

    Main page