All-weather estimates of the land surface skin temperatures from a combined analysis of microwave and infrared satellite observations

Transcription

1 All-weather estimates of the land surface skin temperatures from a combined analysis of microwave and infrared satellite observations Filipe Aires 1,2, Catherine Prigent 1, Carlos Jimenez 1, Julie Catherinot 1, Bill Rossow 3 1 Observatoire de Paris, France 2 Estellus, Paris, France 3 NOAA CREST, CCNY, New York, USA LSA-SAF, Karlsruhe, 06/2013

2 Motivation How to obtain reliable estimates of all-weather Ts? No routine in situ measurements of surface skin temperature (Ts) Ts traditionally measured from thermal infrared - the most direct estimate ~ e IR Ts 4 e IR close to 1 [varying on a rather limited range] but, measurements not possible below clouds - large collection of estimates from different sensors and platforms ( e.g. AVHRR, GOES, MODIS, AIRS, ATSR, MSG, AATSR ) with possible high temporal and spatial resolutions - ongoing activities evaluating the observed Ts differences e.g. Jul 93 AIRS - MODIS night Ts difference [Jimenez et al. (2012), JGR] 2

3 Motivation How to obtain reliable estimates of all-weather Ts? Passive microwave in window channels can (to some extent) penetrate clouds, but - more sensitivity to the emissivity emw, that varies with vegetation, soil moisture Tb ~ emw Ts e.g. Jul GHz emw - lower frequencies have larger penetration depth (in that case, it is not the skin temperature that is measured but the temperature somewhere below the surface) - only polar satellites (e.g. SMMR, SSM/I, AMSR-E, ) and low spatial resolution 3

4 Motivation How to obtain reliable estimates of all-weather Ts? Several attempts to derive Ts using microwave obs, e.g: Njoku (1995) simulations to show that Ts could be retrieved with ~ 2.5K error Basist et al. (1998) investigate correspondences between in situ Tair and observed SSM/I Tbs, taking e MW into account by incorporating a land classification into the regression. Wen and Grody (1998) physical retrieval using two close SSM/I channels to eliminate the e M effect. Prigent and Rossow (1999) simultaneous retrieval of Ts, water vapor and liquid water contents from SSM/I Tbs using a variational method and pre-calculated e MW Aires et al. (2001) neural network inversion to estimate simultaneously Ts, the e MW, and water vapor and liquid water contents from SSM/I Tbs. Holmes et al. (2008): linear regression between the V-Tb 37GHz from passive instruments (SSM/I, AMSR) and in situ longwave fluxes (FLUXNET) to retrieve Ts. 4

5 Methodology Multi-variable retrieval over land from SSM/I inversion method based on a neural network trained with simulated brightness temperatures (Tb) to retrieve simultaneously the Ts, e MW, atmospheric water vapor, and cloud liquid water [Aires et al. (2001), JGR] (a) Building inversion database 3 hours, 30 km, ISCCP Dx dataset [Rossow and Schiffer (1999), BAMS] 6 hours, 2.5 o, NCEP reanalysis [Kalnay et al. (1996), BAMS] 0.25x0.25 o SSM/I monthly mean e MW [Prigent et al. (2006), BAMS] V/H 19, 22(H), 37, 85 GHz containing ~ samples (Jan-Jul 93), 55% for cloudy conditions simulated Tbs without significant bias with real observations (<0.5K) inversion database 5

6 Methodology Multi-variable retrieval over land from SSM/I (b) Training the neural network training phase separated training for clear and cloudy scenes (ISCCP cloud flag) adding a priori information to better constrain the inversion problem (c) Inverting observed SSM/I Tbs operational retrieval +15 years (0.25 o x 0.25 o) of microwave Ts using the SMM/I satellite series 6

7 Methodology Multi-variable retrieval over land from SSM/I - example of retrieved fields from inversion algorithm e.g. June 11th,

8 Evaluation Theoretical retrieval errors - estimated by comparing the retrieved Ts (from simulated Tb) to the original Ts [Aires et al. (2001), JGR] Liquid Water Path RMS NN with a priori NN without a priori Ts error (LWP,e MW ) [K] no significant bias related to cloud cover or surface emissivity only small increase in retrieval error related to cloud cover 8

9 Quality of the retrievals Evaluation - controlled by comparing the observed Tb to the after-inversion simulated Tb (applying the radiative transfer model to the retrieved products) [Prigent et al. (2003), JGR] the retrieval performs well, Tdif of the order of the radiometric noise (~0.6K) a posteriori criterion cloud temperature V H RMS Vertical Pol Horizontal Pol [ Tdif > 2 std ] to filter out non converging retrievals (deep clouds, heavy rain, large ice clouds, snow surfaces) Tdif = 9

10 Evaluation Comparing with in situ Tair - no routine in situ Ts, so comparison with Tair from meteorological network, with Ts-Tair showing all expected variations with solar flux, soil characteristics, and cloudiness. locations of the Tair in situ measurements consistency with diurnal radiative forcing: Day Ts-Tair > 0 [Ts warms more rapidly than Tair] Night Ts-Tair < 0 [Tair cools more rapidly than Ts] also daytime Ts-Tair smaller for cloudy scenes as expected [Prigent et al. (2003), JGR] [SSM/I ascending time 21:00 (F11), (F10)] 10

11 Evaluation Comparing with in situ Tair vegetation used as an indicator of the soil/ vegetation available soil moisture consistency with heat flux controlling: Ts-Tair desert > Ts-Tair vegetation [larger sensible flux] [larger latent flux] in general, Ts-Tair increase from early morning to noon and decrease in the afternoon 11

12 Comparing with in situ Ts Evaluation - comparison with 12 selected stations from the CEOP network [ selected stations [heterogeneous areas] e.g. mid-lat, 2003 Ts from IR (ISCCP), MW (this work, Holmes(2008)), in situ (CEOP) BALTEX-Liendenberg-Falkenberg [Catherinot et al. (2003), JGR] 12

13 Comparing with in situ Ts Evaluation - looking at Ts differences as function of cloudiness and surface emissivity Mid-latitude stations - no obvious impact of the water vapor or cloud in the Ts differences, suggesting reasonable performance under cloudy conditions. - larger impact for Holmes (2008), as the e MW is not implicitly taken into account in the retrieval (working better at the V37GHz predominant e MW =0.95) ) 13

14 Summary Microwave wavelengths, being much less affected by clouds than the infrared, are an attractive alternative in cloudy regions. We developed a inversion technique to retrieve Ts from SSM/I microwave observations based on training a neural network with a realistic database of simulated brightness temperatures (Tb). The inversion algorithm has been used to produce a +15 years record of all-weather global Ts at 0.25 o x0.25 o resolution, with a estimated retrieval error of ~1.5K. The product has been carefully characterized in terms of retrieval errors and post-inversion fit to the observed radiances, and evaluated with clear-sky Ts from infrared sensors, and clear/cloudy Tair (meteorological network) and in situ Ts measurements (CEOP network). Ongoing work to extend the data series, use other sensors, and develop a simpler algorithm to allow NRT processing. 14