Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.

Slides:



Advertisements
Similar presentations
SNPP VIIRS green vegetation fraction products and application in numerical weather prediction Zhangyan Jiang 1,2, Weizhong Zheng 3,4, Junchang Ju 1,2,
Advertisements

Quantification of the sensitivity of NASA CMS-Flux inversions to uncertainty in atmospheric transport Thomas Lauvaux, NASA JPL Martha Butler, Kenneth Davis,
Characterization of ATMS Bias Using GPSRO Observations Lin Lin 1,2, Fuzhong Weng 2 and Xiaolei Zou 3 1 Earth Resources Technology, Inc.
A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
Green Vegetation Fraction (GVF) derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the SNPP satellite Zhangyan Jiang 1,2,
Improving Severe Weather Forecasting: Hyperspectral IR Data and Low-level Inversions Justin M. Sieglaff Cooperative Institute for Meteorological Satellite.
Characterizing and comparison of uncertainty in the AVHRR Pathfinder SST field, Versions 5 & 6 Robert Evans Guilllermo Podesta’ RSMAS Nov 8, 2010 with.
Xin Kong, Lizzie Noyes, Gary Corlett, John Remedios, Simon Good and David Llewellyn-Jones Earth Observation Science, Space Research Centre, University.
Validating the moisture predictions of AMPS at McMurdo using ground- based GPS measurements of precipitable water Julien P. Nicolas 1, David H. Bromwich.
MODIS Regional and Global Cloud Variability Brent C. Maddux 1,2 Steve Platnick 3, Steven A. Ackerman 1,2, Paul Menzel 1, Kathy Strabala 1, Richard Frey.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Correction of Vegetation Time Series for Long Term Monitoring Marco Vargas¹.
Introduction and Methodology Daniel T. Lindsey*, NOAA/NESDIS/STAR/RAMMB Louie Grasso, Cooperative Institute for Research in the Atmosphere
1 Tropical cyclone (TC) trajectory and storm precipitation forecast improvement using SFOV AIRS soundings Jun Tim Schmit &, Hui Liu #, Jinlong Li.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Long-Term Upper Air Temperature.
Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.
Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,
TEMPLATE DESIGN © Assessing the Potential of the AIRS Retrieved Surface Temperature for 6-Hour Average Temperature Forecast.
Orbit Characteristics and View Angle Effects on the Global Cloud Field
Development and evaluation of Passive Microwave SWE retrieval equations for mountainous area Naoki Mizukami.
New Products from combined MODIS/AIRS Jun Li, Chian-Yi Liu, Allen Huang, Xuebao Wu, and Liam Gumley Cooperative Institute for Meteorological Satellite.
Development of AMSU-A Fundamental CDR’s Huan Meng 1, Wenze Yang 2, Ralph Ferraro 1 1 NOAA/NESDIS/STAR/CoRP/Satellite Climate Studies Branch 2 NOAA Corporate.
Development of Severe Weather Products for the GOES-R Advanced Baseline Imager Introduction The Advanced Baseline Imager (ABI) aboard the GOES-R series.
Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. The sensor.
THE NOAA SSU STRATOSPHERIC TEMPERATURE CLIMATE DATA RECORD Cheng-Zhi Zou NOAA/NESDIS/Center For Satellite Applications and Research Haifeng Qian, Lilong.
5. Accumulation Rate Over Antarctica The combination of the space-borne passive microwave brightness temperature dataset and the AVHRR surface temperature.
DEVELOPING HIGH RESOLUTION AOD IMAGING COMPATIBLE WITH WEATHER FORECAST MODEL OUTPUTS FOR PM2.5 ESTIMATION Daniel Vidal, Lina Cordero, Dr. Barry Gross.
On the Use of Geostationary Satellites for Remote Sensing in the High Latitudes Yinghui Liu 1, Jeffrey R. Key 2, Xuanji Wang 1, Tim Schmit 2, and Jun Li.
USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH Joel Susskind University of Maryland May 12, 2005.
Andrew Heidinger and Michael Pavolonis
Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 The GOES-14 Science Test Timothy Schmit (GOVERNMENT PRINCIPAL INVESTIGATOR)
A New Inter-Comparison of Three Global Monthly SSM/I Precipitation Datasets Matt Sapiano, Phil Arkin and Tom Smith Earth Systems Science Interdisciplinary.
Jinlong Li 1, Jun Li 1, Timothy J. Schmit 2, Fang Wang 1, James J. Gurka 3, and W. Paul Menzel 2 1 Cooperative Institute for Meteorological Satellite Studies.
Jinlong Li 1, Jun Li 1, Christopher C. Schmidt 1, Timothy J. Schmit 2, and W. Paul Menzel 2 1 Cooperative Institute for Meteorological Satellite Studies.
High impact weather studies with advanced IR sounder data Jun Li Cooperative Institute for Meteorological Satellite Studies (CIMSS),
As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.
Validation of OMI NO 2 data using ground-based spectrometric NO 2 measurements at Zvenigorod, Russia A.N. Gruzdev and A.S. Elokhov A.M. Obukhov Institute.
Evapotranspiration Estimates over Canada based on Observed, GR2 and NARR forcings Korolevich, V., Fernandes, R., Wang, S., Simic, A., Gong, F. Natural.
Preparing for GOES-R: old tools with new perspectives Bernadette Connell, CIRA CSU, Fort Collins, Colorado, USA ABSTRACT Creating.
Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.
Layered Water Vapor Quick Guide by NASA / SPoRT and CIRA Why is the Layered Water Vapor Product important? Water vapor is essential for creating clouds,
Intercomparison of Polar Cloud Climatology: APP-x, ERA-40, Ground-based Observations Xuanji Wang Cooperative Institute for Meteorological Satellite Studies.
Point Comparison in the Arctic (Barrow N, 156.6W ) Part I - Assessing Satellite (and surface) Capabilities for Determining Cloud Fraction, Cloud.
Retrieval Algorithms The derivations for each satellite consist of two steps: 1) cloud detection using a Bayesian Probabilistic Cloud Mask; and 2) application.
ITSC-1227 February-5 March 2002 Use of advanced infrared sounders in cloudy conditions Nadia Fourrié and Florence Rabier Météo France Acknowledgement G.
SeaWiFS Views Equatorial Pacific Waves Gene Feldman NASA Goddard Space Flight Center, Lab. For Hydrospheric Processes, This.
Analysis of Simultaneous Nadir Observations of MODIS from AQUA and TERRA Andrew Heidinger and many others NOAA/NESDIS Office of Research and Applications.
Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a.
High impact weather nowcasting and short-range forecasting using advanced IR soundings Jun Li Cooperative Institute for Meteorological.
PRELIMINARY VALIDATION OF IAPP MOISTURE RETRIEVALS USING DOE ARM MEASUREMENTS Wayne Feltz, Thomas Achtor, Jun Li and Harold Woolf Cooperative Institute.
TS 15 The Great Salt Lake System ASLO 2005 Aquatic Sciences Meeting Climatology and Variability of Satellite-derived Temperature of the Great Salt Lake.
Variability of Arctic Cloudiness from Satellite and Surface Data Sets University of Washington Applied Physics Laboratory Polar Science Center Axel J.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS LIMB CORRECTION OF POLAR- ORBITING IMAGERY FOR THE IMPROVED INTERPRETATION.
A comparison of AMSR-E and AATSR SST time-series A preliminary investigation into the effects of using cloud-cleared SST data as opposed to all-sky SST.
Changes in the Melt Season and the Declining Arctic Sea Ice
Yinghui Liu1, Jeff Key2, and Xuanji Wang1
Bias Correction of Global Gridded Precipitation for
USING GOES-R TO HELP MONITOR UPPER LEVEL SO2
Winds in the Polar Regions from MODIS: Atmospheric Considerations
Hyperspectral IR Clear/Cloudy
M. Goldberg NOAA/NESDIS Z. Cheng (QSS)
Jeff Key*, Aaron Letterly+, Yinghui Liu+
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
Variability of CO2 From Satellite Retrievals and Model Simulations
Meteosat Second Generation
Hui Liu, Jeff Anderson, and Bill Kuo
Variability of CO2 From Satellite Retrievals and Model Simulations
Evaluation of the MERRA-2 Assimilated Ozone Product
Validation of NOAA-16/ATOVS Products from AAPP/IAPP Packages in Korea
GOES -12 Imager April 4, 2002 GOES-12 Imager - pre-launch info - radiances - products Timothy J. Schmit et al.
Presentation transcript:

Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer Francis 4 1 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin 2 Office of Research and Applications, NOAA/NESDIS 3 Polar Science Center, University of Washington, Seattle, Washington 4 Rutgers University, NewBrunswick, New Jersey Abstract A 17-year time series of clear-sky temperature inversion strength in the Arctic is derived from TOVS data using 2- channel statistical method, and using TOVS retrieved temperature profile product respectively from 1980 through The inter-satellite calibration problem is alleviated by using one retrieval equation for each of the 17 years. The satellite-derived products are compared to the results based on weather station observations. The spatial distribution and temporal changes of monthly mean inversion strength and trends are also shown. 1. Data The radiosonde data in this study is from the Historical Arctic Rawinsonde Archive (HARA), which comprises over 1.5 million vertical soundings of temperature, pressure, humidity and wind, representing all available rawinsonde ascents from Arctic land stations north of 65 o N from the beginning of record through mid The inversion strength, defined as the difference between the surface temperature and the maximum inversion temperature, is derived from the sounding data. The TIROS Operational Vertical Sounder (TOVS) onboard the NOAA series of satellites provides Brightness Temperature (BT) at 7.3 μ m, 11 μ m and 13.3 μ m channels from 1980 to In this study, NOAA-6 ( ), NOAA-7 ( ), NOAA-9 ( ), NOAA-10 ( ), and NOAA-12 ( ) data are used. The spatial resolution of the TOVS data is around 17 km at nadir. The cloud detection tests, which are described by Chedin et al. (1985) and Francis (1994), are applied to the TOVS data to determine clear and cloudy. Rawinsonde soundings are matched with TOVS overpasses, based on time and spatial distance. The operational TOVS Polar Pathfinder (Path-P) dataset provides the temperature profiles retrievals at surface and 13 pressure levels (10, 30, 50, 70, 100, 300, 400, 500, 600, 700, 850, 900, 1000 hPa) with100 km x 100 km spatial resolution, based on which inversion strength can also be derived. 2. Theoretic Basis and Method The peaks of the weighting functions for the 7.3, 11, 13.3 μ m channels are approximately 650 hPa, the surface, and 850 hPa, respectively, as Figure 1 shows. The brightness temperature of the window channel at 11 μ m, BT 11, will be most sensitive to the temperature of the surface. The 7.3 µm water vapor channel brightness temperature, BT 7.3, is most sensitive to temperatures near 650 hPa. The magnitude of the brightness temperature difference (BTD) between the 7.3 μ m and 11 μ m channels, BT 7.3 -BT 11, will therefore be proportional to the strength of temperature difference between the 650 hPa layer and the surface, which is related to the inversion strength. This should also be true for the 13.3 μ m carbon dioxide channel. The inversion strength can be estimated by the linear combination of BT 7.2, BT 11, and the product is called 2-channel inversion strength. Good inter-satellite calibration is essential for climate change studies. In this paper, in situ sounding data are used to alleviate uncertainties in inter-satellite calibration of the different TOVS sensors by deriving a different set of retrieval coefficients for each year. The initial TOVS clear-sky BTs, determined by TOVS cloud detection tests, are converted to inversion strength using the retrieval equations, then the inversion strength is mapped to the 100 km Equal-Area Scalable Earth Grid (EASE-Grid) based on longitude and latitude of the original data on the daily base. Then monthly mean value is calculated based on the daily mean. The monthly trend is also calculated based on the 17-year monthly mean. Inversion strength can also be derived by using the Path-P temperature profiles instead of radiosonde observations. This inversion strength product is called profile inversion strength. Monthly mean and trend are also derived based on this product. Fig. 1. Weighting function for bands at 6.7 μ m, 7.3 μ m, 11 μ m, 13.3 μ m, and 13.6 μ m using the Subarctic Winter standard atmosphere profile. 3. Validation To validate the monthly mean and trends of 2-channel and profile inversion strength, the monthly mean and trends of inversion strength based on the radiosonde data from weather stations are used. The twice-daily temperature profiles from 1980 to 1996 are used to derive the monthly mean and trends of the inversion strength for all sky conditions for some weather stations. Each of these stations has at least 50% of all possible 0000 and 1200 UTC soundings in each month. The monthly mean is the average of all the inversion strength values in that month, and the monthly trend is derived using linear least squares regression based on the monthly mean from 1980 to The inversion strength monthly mean and trends from weather stations, and both 2- chanel and profile monthly mean inversion strength and trend from TOVS data from 1980 through 1996 are compared in Figure 2 and Figure 3, in which the 2-chanel retrieval values are shown as stars, and TOVS retrieved temperature profile based values are shown as diamonds. Considering the different sky conditions, the monthly means of 2-channel inversion strength agree with those from station data very well in the cold season, with mean bias around 0.3 K. The monthly means of profile inversion strength are lower than those from station data. The mean bias is around –1.5 K, and the biases are as high as –5.5 for some individual stations. The trends from 2-channel and profile methods agree with the trends from station data reasonably well. Acknowledgments This research was supported by NOAA and NSF grants OPP and OPP The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision. Fig. 2. Comparison of monthly mean Inversion Strength (INVST) from weather stations, and from TOVS data in November, and December, January, February, and March over The star symbols represent the results from the 2-channel statistical method. The diamond symbols represent the results from the TOVS retrieved temperature profiles. Fig. 3. Comparison of Inversion Strength (INVST) trends from weather stations and from TOVS data in November, and December, January, February, and March over The star symbols represent the results from the 2-channel statistical method. The diamond symbols represent the results from the TOVS retrieved temperature profiles. 4. Results Figure 4 gives the spatial distribution of monthly mean 2-channel inversion strength in Arctic under clear-sky condition in November, December, January, February, March, and winter (DJF) averaged over the period The spatial distributions of 2-channel inversion strength have similar pattern in Arctic from November to March, but with different magnitudes. The monthly trend of the 2- channel inversion strength over Arctic region under clear conditions in November, December, January, February, March, and winter (DJF) from is shown in Figure 5 based on the monthly mean data, and the trend with confidence level larger than 90% based on F test is labeled with + symbol in the figure. Fig. 4. Monthly mean clear-sky inversion strength (K) in November, December, January, February, March, and winter (DJF) over using 2-channel statistical method. Fig. 5. Monthly trend of clear-sky inversion strength (K/Year) in November, December, January, February, March, and winter (DJF) over using 2-channel statistical method. The trend with confidence level larger than 90% based on F test is labeled with +.