GEWEX-GDAP/CGMS-IPWD PRECIPITATION ASSESSMENT

Lead: Dong-Bin Shin and Ali Behrangi

3. Consistency • Global Water budget • Regional/seasonal • Precipitation regime • Application/hydrology/extreme analysis

We have collected about 35 papers so far in support of our discussion and we can share that with you to complete the publication list…

Global water and energy budget

Stephens et al. 2012:An update on Earth's energy balance in light of the latest globalobservations. Nature Geoscience, 5, 691-696.

Trenberth et al. 2007:Estimates of the Global Water Budget and Its Annual Cycle Using Observational

and Model Data. Journal of Hydrometeorology, 8, 758-769.

L’Ecuyer et al. 2015: The Observed State of the Energy Budget in the Early Twenty-First Century. Journal of Climate, 28, 8319-8346

Rodell et al. 2015:The Observed State of the Water Cycle in the Early Twenty-First Century. Journal of Climate, 28,

Using a variational method is introduced that explicitly accounts for the relative accuracies in all component fluxes

residual heat flux into the oceans of 0.45Wm-2

4.7%

Behrangi et al. 2014 = 4.3%15% increase

Precipitation remains as a major source of uncertainty in our W/E budget analysis. It is important to realize that what we do significantly impacts global W&E studies 5% up or down in precip => is about 4W/m2

Question: Shall we check the water/energy cycle closure as we

retrieve our products or the closure check should remain independent of our retrieval method?

(e.g., a good example is changes in DPR/PR radar reflectivity that may change global precipitation by X percent)

Adler et al. 2012:Estimating Climatological Bias Errors for the Global Precipitation ClimatologyProject (GPCP). Journal of Applied Meteorology and Climatology, 51, 84-99.

Skofronick-Jackson et al. 2017: The Global Precipitation Measurement (GPM) Mission for Science and Society. Bulletin of the American Meteorological Society, 98, 1679-1695.

estimated relative bias error

Regional/Seasonal consistency

Tian et al. 2010:A global map of uncertainties in satellite-based precipitation measurements. Geophysical Research Letters, 37,

Winter

Spring

Summer

fall

Literature has identified regions that Satellite products are weak at. There has been a move toward using reanalysis/models in our remote sensing products, especially in high latitudes

and mountainous regions

Behrangi, et al., 2016&2018:- Status of high-latitude precipitation estimates from observations and reanalyses.

Journal of Geophysical Research: Atmospheres, 121, 4468-4486.- Using GRACE to Estimate Snowfall Accumulation and Assess Gauge Undercatch Corrections in High Latitudes. Journal of Climate, 31, 8689-8704.

Question: What is the best strategy ? At what level we should allow models/reanalysis leak

into our observation products?

Elsaesser et al. 2010Observed Self-Similarity of Precipitation Regimes over the Tropical Oceans. Journal of Climate, 23, 2686-2698.

Adler et al. 2017 Global Precipitation: Means, Variations and Trends During the Satellite Era (1979–2014). Surveys in Geophysics, 38, 679-699. (also PMM18 presentation)

Wong and Behrangi, 2018:Regime-Dependent Differences in Surface Freshwater Exchange Estimates

Over the Ocean. Geophysical Research Letters, 45, 955-963.

Berg, W., T. L'Ecuyer, and C. Kummerow, 2006: Rainfall Climate Regimes: The Relationship of Regional TRMM Rainfall Biases to the Environment. Journal of Applied Meteorology and Climatology, 45, 434-454.

Linking bias to physical variables that can lead to a better understanding of the mechanisms responsible for the observed differences

Precipitation regime & Consistency

Can we determine effective environment regimes to maximize efficiency in evaluation

and feedback to retrieval methods? (TPW, T, water vapor convergence, CS, etc.)?

Along this line, are we in need of a critical observation that we currently don’t have?

(e.g., 3D wind)

It seems critical to have our regime-based precipitation analysis?

Can we use this as a framework that can bring together different evaluation studies?

The battle between consistency, accuracy, and resolution - Application/hydrology is asking for higher resolution products (e.g., for extreme analysis)- Precipitation community produces precipitation products at (4km, 5km, 10km, 25km, etc.)- We know there is a limit to our information content ( but we keep producing higher res.)- As a result users keep using the highest resolution offered (at the expense of accuracy and

consistency among products)

Herold, N., Behrangi, A., & Alexander, L. V. (2017). Large uncertainties in observed daily precipitation extremes over land. GRL, 122(2), 668-681.

GPCP-1DD, TMPA, PERSIANN-CDRCHIRPSGPCC

(Spr

ead)

Reference:

ETCCDI: Expert Team on Climate Change and Detection Indices

The point:Spread among products becomes very large at higher resolution. So:(1) to go to higher resolution shall we think about adding information content in high resolution (e.g., think about higher res. observing systems with direct precipitation-related info)?(2) How about bringing other observations that we have not used (lightning, multi-spectral, etc.)?