PGE06 TPW Total Precipitable Water

Slides:



Advertisements
Similar presentations
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
Advertisements

SAFNWC/GEO package: An overview
CDOP NWC SAF Workshop on Physical Retrieval Gabriela Cuevas Tascón, INM (Spain) November, 28th CDOP NWCSAF Workshop on Physical Retrieval.
New Product to Help Forecast Convective Initiation in the 1-6 Hour Time Frame Meeting September 12, 2007.
Meteorology is the study of weather, weather phenomena, and weather forecasting. It uses two kinds of observations: Qualitative: not based on numbers.
Unit 2 World Climate Patterns… An Introduction. Distinguish between the terms weather & climate. P. 54 Weather = the state of the atmosphere at any one.
Presenting NWCSAF products, activities by EUMeTrain Mária Putsay and Andreas Wirth Hungarian Meteorological Service ZAMG NWC SAF 2015 Users’ Workshop
Satellite SST Radiance Assimilation and SST Data Impacts James Cummings Naval Research Laboratory Monterey, CA Sea Surface Temperature Science.
Moist Processes ENVI1400: Lecture 7. ENVI 1400 : Meteorology and Forecasting2 Water in the Atmosphere Almost all the water in the atmosphere is contained.
16/06/20151 Validating the AVHRR Cloud Top Temperature and Height product using weather radar data COST 722 Expert Meeting Sauli Joro.
Atmospheric Emission.
Xin Kong, Lizzie Noyes, Gary Corlett, John Remedios, Simon Good and David Llewellyn-Jones Earth Observation Science, Space Research Centre, University.
Surface Skin Temperatures Observed from IR and Microwave Satellite Measurements Catherine Prigent, CNRS, LERMA, Observatoire de Paris, France Filipe Aires,
Satellite basics Estelle de Coning South African Weather Service
ECMWF – 1© European Centre for Medium-Range Weather Forecasts Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF Heather Lawrence, first-year.
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.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 CLOUD MASK AND QUALITY CONTROL FOR SST WITHIN THE ADVANCED CLEAR SKY PROCESSOR.
Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.
Global Warming Cause for Concern. Cause for Concern? What is the effect of increased levels of carbon dioxide in the Earth’s atmosphere? Nobody knows.
Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.
USING OF METEOSAT SECOND GENERATION HIGH RESOLUTION VISIBLE DATA FOR THE IMPOVEMENT OF THE RAPID DEVELOPPING THUNDERSTORM PRODUCT Oleksiy Kryvobok Ukrainian.
TOPIC 7. What is weather? Weather is the state or condition of the variables of the atmosphere at any given location for a short period of time.
USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH Joel Susskind University of Maryland May 12, 2005.
Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal.
1 RTM/NWP-BASED SST ALGORITHMS FOR VIIRS USING MODIS AS A PROXY B. Petrenko 1,2, A. Ignatov 1, Y. Kihai 1,3, J. Stroup 1,4, X. Liang 1,5 1 NOAA/NESDIS/STAR,
Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.
© Imperial College LondonPage 1 Estimating the Saharan dust loading over a west African surface site GIST 26: May 2007.
Radiative transfer in the thermal infrared and the surface source term
1 Validation for CRR (PGE05) NWC SAF PAR Workshop October 2005 Madrid, Spain A. Rodríguez.
METEOSAT SECOND GENERATION FROM FIRST TO SECOND GENERATION METEOSAT
SAF - Nowcasting Product Assessment Review Worshop (Madrid 17 – 18 – 19 0ctober 2005 Yann Guillou Météo-France (DPR) Page 1/8 Long duration validation.
Charles L Wrench RCRU Determining Cloud Liquid Water Path from Radiometer measurements at Chilbolton.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
An Overview of Satellite Rainfall Estimation for Flash Flood Monitoring Timothy Love NOAA Climate Prediction Center with USAID- FEWS-NET, MFEWS, AFN Presented.
A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division.
1 Atmospheric Radiation – Lecture 13 PHY Lecture 13 Remote sensing using emitted IR radiation.
Matthew Lagor Remote Sensing Stability Indices and Derived Product Imagery from the GOES Sounder
Atmosphere-ocean interactions Exchange of energy between oceans & atmosphere affects character of each In oceans –Atmospheric processes alter salinity.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS LIMB CORRECTION OF POLAR- ORBITING IMAGERY FOR THE IMPROVED INTERPRETATION.
© Crown copyright Met Office Assimilating infra-red sounder data over land John Eyre for Ed Pavelin Met Office, UK Acknowledgements: Brett Candy DAOS-WG,
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
Updates on CMA Meteorological Satellite Programs
The Convective Rainfall Rate in the NWCSAF
SPLIT-WINDOW TECHNIQUE
Comparing a multi-channel geostationary satellite precipitation estimator with the single channel Hydroestimator over South Africa Estelle de Coning South.
PGE05 CRR Convective Rainfall Rate
EASC 11 Clouds and Precipitation
CMa & CT Cloud mask and type
Best practices for RGB compositing of multi-spectral imagery
Surface Pressure Measurements from the NASA Orbiting Carbon Observatory-2 (OCO-2) Presented to CGMS-43 Working Group II, agenda item WGII/10 David Crisp.
CTTH Cloud Top Temperature and Height
EASC 11 Chapters 14-18: The Atmosphere
LPW & SAI Layer Precipitable Water and Lifted Index
Vicarious calibration by liquid cloud target
Winds in the Polar Regions from MODIS: Atmospheric Considerations
Solar Energy Chapter 22.2.
Sea Surface Temperature
Fig. 2 shows the relationship between air temperature and relative humidity. 2 (a) (i) Describe the relationship shown in Fig. 2. [3] (ii) State.
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.
The use of SAFNWC products at Instituto de Meteorologia (IM), Portugal
GOES -12 Imager April 4, 2002 GOES-12 Imager - pre-launch info - radiances - products Timothy J. Schmit et al.
Validation for TPW (PGE06)
Development of inter-comparison method for 3.7µm channel of SLSTR-IASI
Front page of the realtime GOES-12 site, showing all of the latest Sounder spectral bands (18 infrared and 1 visible) over the central and Eastern US All.
Water Vapour Imagery and
大气折射与天线指向.
World Climate Patterns… An Introduction
The Atmosphere and Weather
Presentation transcript:

PGE06 TPW Total Precipitable Water NWC SAF First Joint Training Workshop 14-16 June 2004 Madrid, Spain P. Fernández, J.M. Vindel

Total Precipitable Water goal Total Precipitable Water (TPW) is the amount of liquid water, in mm, if all the atmospheric water vapour in the column were condensed. High values of TPW in clear air often become antecedent conditions prior to the development of heavy precipitation and flash floods. When high TPW values areas present a lifting mechanism and warm advection in low levels, heavy precipitation often occurs. These data can provide to forecasters an important tool for very short range forecasting. Within the SAF NWC context, the main goal is to provide TPW data in clear air pixel by pixel in image format for Nowcasting purposes.

Total Precipitable Water algorithms The algorithms assume the lower and mid troposphere as a thick layer at temperature Tair overlying a surface with skin temperature Tsfc. The brightness temperature T for a window IR channel observed at Zenith angle  is a combination of Tsfc and Tair weighted by the channel transmittance  which has a contribution from the carbon dioxide absorption  and water vapour absorption  T = Tsfc *  + Tair * ( 1 - )                              (1) Assuming the transmittance difference proportional to the water vapour content:  = EXP [ - ( +  * PW ) ] * sec                      (2) And approximating the transmittance, assuming low carbon dioxide absorption:  = 1 -  * PW * sec                                       (3) -

Total Precipitable Water algorithms Two different approaches for the estimation of the TPW can be derived: When the Surface Temperature can be accurately obtained (over SEA), by subtracting IR10.8 and IR12.0 channels brightness temperature in Equation (1) we have: T10.8 - T12.0 = ( Tsfc - Tair ) *  By applying Equation (3), and assuming Tair the SEVIRI channel IR13.4 brightness temperature and Tsfc a SST obtained from a Split Window algorithm, the TPW algorithm becomes the Split-Window Difference Algorithm called SD algorithm: TPW = B + A * [ (T10.8 - T12.0) / (SST – T13.4) ] * cos            (4) -

Total Precipitable Water algorithms 2. When the Surface Temperature accuracy cannot be assured, using Equations (1) and (2) applied to the IR10.8 and IR12.0 channels and eliminating Tsfc, the difference in brightness temperature is related to the differential absorption projected along the Zenith angle: ( T10.8 - Tair)  /  ( T12.0 - Tair ) = EXP [ (  +   * PW ) * sec ] Assuming Tair the SEVIRI channel IR13.4 brightness temperature, the resulting algorithm  for TPW over LAND called Logarithm Ratio or LR Algorithm is: TPW = B + A * LN [ (T10.8 – T13.4) / ( T12.0 – T13.4) ] * cos           (5) -

Total Precipitable Water algorithms Coefficients have been obtained with real SEVIRI data from October, November and December 2003 using Radio Soundings for LAND and ECMWF data for SEA Land Algorithm: TPW (mm)= B + A * LN [ (T10.8 – T13.4) / ( T12.0 – T13.4) ] * cos     With coefficients: Sea Algorithm: TPW (mm)= B + A * [ (T10.8 - T12.0) / (SST – T13.4) ] * cos  

Total Precipitable Water inputs and output SEVIRI data at full IR spatial resolution: T10.8 m, T12.0 m & T13.4 m Auxiliary data: CMa, TPW (from previous slot for QC) Ancillary data: Land/Sea atlas, Sun Zenith Angle & Satellite Zenith Angle OUTPUT: The output is an image in HDF-5 format with TPW values in mm in clear areas and enhanced IR10.8 in cloudy areas.

TPW Loop

TPW day/night gap

TPW calibration changes impact: 30th March 2004 13:45 14:00

TPW calibration changes impact: 13th May 2004 07:30 07:45 08:00

Calibration changes impact in TPW over SEA: IR10.8 22% average 60% average The variation is less than 10% over LAND

TPW examples and limitations 27th May 2004 Sea values are mainly lower than actual Night land values lower than actual Higher values than actual in cloud edges 00:00 12:00

TPW examples and limitations 7th June 2004 Very low values when dust contamination LAND/SEA gap due to lower values over sea Relatively high TPW values prior to convection ECMWF TPW features mainly agrees with TPW SEVIRI data 12:00 18:00

Conclusions The PGE06 TPW has to be re-calibrated using a full year data Coefficients variability over LAND has to be monitored in function of the sun zenith angle The use of WV SEVIRI channels has to be studied The improvement over SEA includes an improvement of the SST algorithm (O&SI SAF) Taking in account the current limitations, the TPW relative values obtained can be useful in situations prior to convection