Impact of atmospheric infrared sounder observations on weather forecasts

Eos ◽  
2005 ◽  
Vol 86 (11) ◽  
pp. 109-116 ◽  
Author(s):  
J. Le Marshall ◽  
J. Jung ◽  
J. Derber ◽  
R. Treadon ◽  
S. J. Lord ◽  
...  
Keyword(s):  
Atmospheric Infrared Sounder ◽  
Weather Forecasts

The impact of atmospheric infrared sounder (AIRS) profiles on short-term weather forecasts

2007 ◽  
Author(s):  
Bradley T. Zavodsky ◽  
Shih-Hung Chou ◽  
Gary Jedlovec ◽  
William Lapenta
Keyword(s):  
Short Term ◽  
Atmospheric Infrared Sounder ◽  
Weather Forecasts ◽  
The Impact

Multivariate Minimum Residual Method for Cloud Retrieval. Part I: Theoretical Aspects and Simulated Observation Experiments

2014 ◽  
Vol 142 (12) ◽  
pp. 4383-4398 ◽  
Author(s):  
Thomas Auligné
Keyword(s):  
Three Dimensional ◽  
New Method ◽  
Cloud Detection ◽  
Atmospheric Infrared Sounder ◽  
Detection Scheme ◽  
Weather Forecasts ◽  
Independent Information ◽  
Medium Range ◽  
Minimum Residual ◽  
Infrared Radiances

Abstract A new method is presented for cloud detection and the retrieval of three-dimensional cloud fraction from satellite infrared radiances. This method, called multivariate minimum residual (MMR), is inspired by the minimum residual technique by Eyre and Menzel and is especially suitable for exploiting the large number of channels from hyperspectral infrared sounders. Its accuracy is studied in a theoretical framework where the observations and the numerical model are supposed perfect. Of particular interest is the number of independent information that can be found on the cloud according to the number of channels used. The technical implementation of the method is also briefly discussed. The MMR scheme is validated with the Atmospheric Infrared Sounder (AIRS) instrument using simulated observations. This new method is compared with the cloud-detection scheme from McNally and Watts that is operational at the European Centre for Medium-Range Weather Forecasts (ECMWF) and considered to be the state of the art in cloud detection for hyperspectral infrared sounders.

scholarly journals Evaluation of upper tropospheric humidity forecasts from ECMWF using AIRS and CALIPSO data

2008 ◽  
Vol 8 (5) ◽  
pp. 17907-17937 ◽  
Author(s):  
N. Lamquin ◽  
K. Gierens ◽  
C. J. Stubenrauch ◽  
R. Chatterjee
Keyword(s):  
Relative Humidity ◽  
Satellite Observation ◽  
Correction Method ◽  
Radiosonde Data ◽  
Atmospheric Infrared Sounder ◽  
Weather Forecasts ◽  
Medium Range ◽  
Upper Tropospheric Humidity ◽  
One Year ◽  
Humidity Profiles

Abstract. An evaluation of the upper tropospheric humidity from the European Centre of Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) is presented. We first make an analysis of the spinup behaviour of ice supersaturation in weather forecasts. It shows that a spinup period of at least 12 h is necessary before using forecast humidity data from the upper troposphere. We compare the forecasted upper tropospheric humidity with coincident relative humidity fields retrieved from the Atmospheric InfraRed Sounder (AIRS) and with cloud vertical profiles from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The analysis is made over one year, from October 2006 to September 2007, and we discuss how relative humidity and cloud features appear both in the IFS and in the observations. In a last part, we investigate the presence of ice supersaturation within low vertical resolution pressure layers by comparing the IFS outputs for high-resolution and low-resolution humidity profiles and by simulating the interpolation of humidity over radiosonde data. A new correction method is proposed and tested with these data.

scholarly journals Comparisons of the tropospheric specific humidity from GPS radio occultations with ERA–Interim, NASA MERRA and AIRS data

2017 ◽  
Author(s):  
Panagiotis Vergados ◽  
Anthony J. Mannucci ◽  
Chi O. Ao ◽  
Olga Verkhoglyadova ◽  
Byron Iijima
Keyword(s):  
Specific Humidity ◽  
Data Sets ◽  
Atmospheric Infrared Sounder ◽  
Trade Winds ◽  
Latitude Belt ◽  
Weather Forecasts ◽  
Global Positioning ◽  
Data Record ◽  
Modern Era

Abstract. We construct a 9–year data record (2007–2015) of the tropospheric specific humidity (SH) using Global Positioning System radio occultation (GPS RO) observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. This record covers the ±40° latitude belt and includes estimates of the zonally averaged monthly mean SH from 700 hPa up to 400 hPa. It includes three major climate zones: a) the deep tropics (±15°), b) the trade winds belts (±15–30°), and c) the subtropics (±30–40°). Our objective is to compare the RO observations with the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim), the Modern-Era Retrospective analysis for Research and Applications (MERRA), and the Atmospheric Infrared Sounder (AIRS) to examine the consistency among the data sets. We present RO SHs from both JPL and UCAR processing centers to provide an estimate of the structural uncertainty of the RO SH products. The results show that the RO observations capture the seasonal and interannual SH variability as all other data sets. On average, the JPL-RO SH agrees with both reanalyses to within 10 %, is overall larger than all data sets, having maximum differences with AIRS by ~ 10–30 %, and is almost twice as wet as all other data sets in the middle-to-upper troposphere at the subtropics. The UCAR-RO SH also agrees with both reanalyses and AIRS, but is systematically drier than all other data sets. Provided the estimated differences between the RO observations and the rest of the data sets, together with the retrieval uncertainty of the SH products from all data sets, we conclude that RO observations are a valuable independent observing system, which could augment independent reanalyses and satellite platforms. We anticipate that the COSMIC-2 mission will increase the observational sampling; thus, improving the coverage and quality of the observed SH climatology.

scholarly journals Evaluation of upper tropospheric humidity forecasts from ECMWF using AIRS and CALIPSO data

2009 ◽  
Vol 9 (5) ◽  
pp. 1779-1793 ◽  
Author(s):  
N. Lamquin ◽  
K. Gierens ◽  
C. J. Stubenrauch ◽  
R. Chatterjee
Keyword(s):  
Relative Humidity ◽  
Satellite Observation ◽  
Correction Method ◽  
Radiosonde Data ◽  
Vertical Resolution ◽  
Atmospheric Infrared Sounder ◽  
Weather Forecasts ◽  
Clear Sky ◽  
Upper Tropospheric Humidity ◽  
One Year

Abstract. An evaluation of the upper tropospheric humidity from the European Centre of Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) is presented. We first make an analysis of the spinup behaviour of ice supersaturation in weather forecasts. It shows that a spinup period of at least 12 h is necessary before using forecast humidity data from the upper troposphere. We compare the forecasted upper tropospheric humidity with coincident relative humidity fields retrieved from the Atmospheric InfraRed Sounder (AIRS) and with cloud vertical profiles from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The analysis is made over one year, from October 2006 to September 2007, and we discuss how relative humidity and cloud features appear both in the IFS and in the observations. The comparison with AIRS is made difficult because of the vertical resolution of the sounder and the impossibility to retrieve humidity for high cloudiness. Clear sky relative humidities show a rather good correlation whereas cloudy situations reflect more the effect of a dry bias for AIRS increasing with the relative humidity. The comparison with CALIPSO shows that the IFS predicts high relative humidity where CALIPSO finds high clouds, which supports the good quality of the ECMWF upper tropospheric cloud forecast. In a last part, we investigate the presence of ice supersaturation within low vertical resolution pressure layers by comparing the IFS outputs for high-resolution and low-resolution humidity profiles and by simulating the interpolation of humidity over radiosonde data. A new correction method is proposed and tested with these data.

scholarly journals Comparisons of the tropospheric specific humidity from GPS radio occultations with ERA-Interim, NASA MERRA, and AIRS data

2018 ◽  
Vol 11 (2) ◽  
pp. 1193-1206 ◽  
Author(s):  
Panagiotis Vergados ◽  
Anthony J. Mannucci ◽  
Chi O. Ao ◽  
Olga Verkhoglyadova ◽  
Byron Iijima
Keyword(s):  
Specific Humidity ◽  
Data Sets ◽  
Atmospheric Infrared Sounder ◽  
Trade Winds ◽  
Climate Zones ◽  
Latitude Belt ◽  
Weather Forecasts ◽  
Modern Era ◽  
Interannual Anomaly ◽  
Good Agreement

Abstract. We construct a 9-year data record (2007–2015) of the tropospheric specific humidity using Global Positioning System radio occultation (GPS RO) observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. This record covers the ±40∘ latitude belt and includes estimates of the zonally averaged monthly mean specific humidity from 700 up to 400 hPa. It includes three major climate zones: (a) the deep tropics (±15∘), (b) the trade winds belts (±15–30∘), and (c) the subtropics (±30–40∘). We find that the RO observations agree very well with the European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-Interim), the Modern-Era Retrospective Analysis for Research and Applications (MERRA), and the Atmospheric Infrared Sounder (AIRS) by capturing similar magnitudes and patterns of variability in the monthly zonal mean specific humidity and interannual anomaly over annual and interannual timescales. The JPL and UCAR specific humidity climatologies differ by less than 15 % (depending on location and pressure level), primarily due to differences in the retrieved refractivity. In the middle-to-upper troposphere, in all climate zones, JPL is the wettest of all data sets, AIRS is the driest of all data sets, and UCAR, ERA-Interim, and MERRA are in very good agreement, lying between the JPL and AIRS climatologies. In the lower-to-middle troposphere, we present a complex behavior of discrepancies, and we speculate that this might be due to convection and entrainment. Conclusively, the RO observations could potentially be used as a climate variable, but more thorough analysis is required to assess the structural uncertainty between centers and its origin.

scholarly journals Multivariate Minimum Residual Method for Cloud Retrieval. Part II: Real Observations Experiments

2014 ◽  
Vol 142 (12) ◽  
pp. 4399-4415 ◽  
Author(s):  
Thomas Auligné
Keyword(s):  
Cloud Fraction ◽  
Cloud Detection ◽  
Atmospheric Infrared Sounder ◽  
Detection Scheme ◽  
Weather Forecasts ◽  
Cloud Systems ◽  
Medium Range ◽  
Minimum Residual ◽  
Infrared Radiances ◽  
High Chance

Abstract In Part I of this two-part paper, the multivariate minimum residual (MMR) scheme was introduced to retrieve profiles of cloud fraction from satellite infrared radiances and identify clear observations. In this paper it is now validated with real observations from the Atmospheric Infrared Sounder (AIRS) instrument. This new method is compared with the cloud detection scheme presented earlier by McNally and Watts and operational at the European Centre for Medium-Range Weather Forecasts (ECMWF). Cloud-top pressures derived from both algorithms are comparable, with some differences at the edges of the synoptic cloud systems. The population of channels considered as clear is less contaminated with residual cloud for the MMR scheme. Further procedures, based on the formulation of the variational quality control, can be applied during the variational analysis to reduce the weight of observations that have a high chance of being contaminated by cloud. Finally, the MMR scheme can be used as a preprocessing step to improve the assimilation of cloud-affected infrared radiances.

Reducing Probabilistic Weather Forecasts to the Worst Case Scenario: Anchoring Effects

2008 ◽  
Author(s):  
Sonia Savelli ◽  
Susan Joslyn ◽  
Limor Nadav-Greenberg ◽  
Queena Chen
Keyword(s):  
Case Scenario ◽  
Worst Case ◽  
Weather Forecasts ◽  
Worst Case Scenario ◽  
Anchoring Effects
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