National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Advancing global precipitation measurement in light of A-Train observations Ali Behrangi NASA Jet Propulsion Laboratory, California Institute of Technology Atmospheric Physics and Weather Group M. Richardson, G. Huffman, R. Adler, G. Stephens , M. Lebsock Also thanks to : Matthew Christensen, Norm Wood and Tristan L'Ecuyer, John Haynes Bjorn Lambrigtsen, Eric Fetzer A-Train Symposium 2017, 19-21 April 2017 Pasadena, CA

Estimated relative bias error Precipitation measurement – status GPCC total number of stations per 1 deg grid (April 2009) Estimated relative bias error Estimated relative bias error calculated based on spread of climatology products over their mean, which exceeds 50% at higher latitudes, especially over ocean. The figures are from Adler et al. (2012). The actual errors are likely higher than that shown here because the climatology products used in the calculation are not independent 1mm/d 29 w/m2 Adler et al. (2012).

Zonal precipitation phase frequency from CloudSat Zonal precipitation occurrence distribution is not symmetric (snow+ mixed phase) is the dominant type of precipitation Poleward of ~50 deg latitude 30% of global precip. occurs poleward 40 lat. (23% for 45lat)

Regional precipitation frequency from CloudSat LAND OCEAN Snowfall Mixed phase Snowfall Mixed phase Behrangi et al. Water resources research (WRR; 2014) Behrangi et al. J. of climate (2014)

Scaling and sampling consideration Behrangi et al. (JGR; 2012)

Combined precipitation : CloudSat, TRMM PR & AMSR Behrangi et al. (2012) Berg et al. (2010)

Comparison with GPCP and CMAP TRMM Mean precipitation rate (mm/day) MCTA GPCP CMAP CloudSat Merged CloudSat-TRMM-AMSR estimate AMSR-E Difference (mm/d) Relative diff Behrangi et al. (J. of Climate, 2014) Latitude

Missing precipitation over ocean (TRMM era) Fraction 0 0.1 0.2 0.3 0.4 0.5 0 1.2 2.4 3.6 mm/day Behrangi et al. (JGR; 2012)

-- TRMM Zonal distribution of precipitation ocean (pre-GPM) mm/day /data/home/abehrang/A/codes/PAPER_GPM_CS/agu_ocean_plots.m GPCP V2.2 showed ~28% more precipitation than AMSR-E (~24 W/km2) !

CloudSat contribution ocean MCTA: Merged CloudSat TRMM(PR) Aqua (AMSR) Behrangi et al. (2014; J of climate) mm/day It was found that GPCP may underestimate global precipitation by about 5% The missed 5% precip. matched the water budget closure study by Rodell et al. (2015) performed a year later.

CloudSat contribution (update) ocean MCTA-V2= MCTA + mixed phase precipitation mm/day

CloudSat contribution (update) ocean GPCP V2.3 mm/day /data/home/abehrang/A/codes/PAPER_GPM_CS/agu_ocean_plots.m

CloudSat contribution (update) ocean AMSR-2 GPM GPROF 2014 V4 mm/day /data/home/abehrang/A/codes/PAPER_GPM_CS/agu_ocean_plots.m MCTA V2 shows about 5.5% more precipitation than GPCP V2.3 over ocean MCTA V2 shows ~23% more precipitation than new retrievals from AMSR2 (it was 33% AMSR-E).

GPM assessment Gail Skofronick-Jackson et al. BAMS 2017

GPM assessment Land mm/day AMSR-2 GPM GPROF 2014 V4 GPCP V2.3 …, although we expect additional improvement by GPROF V5

Analysis based on environmental conditions

Missing precipitation over land (TRMM era) Comparison of precipitation detectability as a function of cloud type Fraction of precipitation detected by CloudSat (TRMM era) LAND Behrangi et al. 2014 Over land : MW precipitation are robust for: Convective system Rainfall over non-frozen land

High latitude precipitation map NH Given that the products have been in state of flux and in preparation to more effectively assess the impact of GPM we started to analyze various precipitrion pocuts and identify zones of disagreement. Gradient of precipitation amount Higher precip is seen in reanalysis and cloudsat comapred to GPCP Reanalysis and cloudsat show similar patterns CMAP significantly underestiamtes and has missing data (2007-2010) Behrangi et al. 2016 (JGR)

GRACE contribution ! T2m <-1 C Behrangi et al. (GRL, 2017) T2m <-1 C Behrangi et al. (GRL, 2017) Behrangi et al. (JGR, 2016)

? High latitude precipitation maps SH Behrangi et al. 2016 (JGR) CMAP very low GPCP around 60 degree shows little sensitivity to changes in longitude CloudSat and reanalyses have similar patterns NCEP tends to over estimate Behrangi et al. 2016 (JGR)

Summary of statistics for high latitude (poleward 50deg S) Land+ Ocean Ocean Land (Antarctica) Behrangi et al. 2016 JGR

CMIP5 Model ranks based on CloudSat observation: Rank 1 is the best match. SH ocean BIAS SH ocean RMSE SH land BIAS SH land BIAS

Seasonal future changes in precipitation The subset of CMIP5 models that best match current CloudSat estimate shows larger increase in future precipitation (i.e., the end of 21st century) than all-model ensemble average. This rate depends on region and season. Also see Palerme et al. 2016 for assessing CMIP5 models precip. based on CloudSat snowfall

Summary CloudSat has brought great insights on precipitation occurrence, intensity, and phase, globally and especially in high latitudes. Through Merging CloudSat, TRMM, and AMSR (MCTA) we found that about 5% of global precipitation is likely missed by GPCP => ~4 W/m2 In fact, this estimate was later confirmed by water closure study by Rodell et al. (2015) MCTA suggests that GPCP is relatively robust in capturing the global numbers, but there are inconsistencies in zonal and regional distribution of precipitation amount (also confirmed by GRACE). Efforts are underway to improve GPCP in high latitudes. CloudSat is now helping GPM for both assessment and retrieval purposes, especially in high latitudes. In fact, it is being used to guide GPROF. A Subset of CMIP5 models that best match current CloudSat estimate shows larger increase in future precipitation (i.e., the end of 21st century) than all-model ensemble average. This rate depends on region and season.

Thanks! E-mail: ali.behrangi@jpl.nasa.gov