Global View of Real-time TRMM Multi-satellite Precipitation Analysis (TMPA)

International Precipitation Working Group 7 --- Tsukuba, Japan

Bin Yong1, Die Liu1, Jonathan J. Gourley2, Yudong Tian3, George J. Huffman4,

Liliang Ren1, Yang Hong5

1 State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China 2 NOAA/National Severe Storms Laboratory, Norman, Oklahoma, USA. 3 University of Maryland, College Park, Maryland, USA. 4 NASA Goddard Space Flight Center, Greenbelt, Maryland, USA. 5 University of Oklahoma, Norman, OK 73019, USA Session 12, Validation II, Wednesday, 19 November, 2014, Tsukuba International Congress Center

River Sea We are the hydrological user of GPM in China

1. Our previous works in hydrology

3. Summary

OUTLINE

2. Evaluation of the climatological calibration algorithm in real-time TMPA

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Upper boundary: temperature/precipitation, weather, climate [Climate Change ?! ]

Lower boundary: topography, vegetation, geology, soil structure, …… [Human Activities ?! ]

Hydrological Cycle at Basin scale

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死亡1481人 损失1666亿人民币

Yangtzi River Flood 1998 Zhouqu Landslide 2010

Rainfall-triggered Nature Hazards in China

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Introduction of Chinese Basins

9 large basins

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3 Validation Basins and 1 Testing Basin for GPM Hydrological Evaluation

The rain gauge networks in these basins are maintained by the Chinese Ministry of Water Resources (CMWR). They are dependent from the China Meteorological Administration (CMA) gauges used for GPM gauge-adjustment.

The CMWR gauges are denser than CMA gauges, but they are more difficult to get.

We did some field investigation in these basins in the past ten years.

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For Example

(More than 10 years’ investigation)

Soil moisture measuring points :

20

Surface temperature measuring points :

28

Remote anchor points :44

Land use/cover anchor points : 28

(28个)

Ecological quadrats : 10

Tree ring measuring points :

36

Field observation points : 218

Field scientific investigation data in Laohahe Basin

LAI measuring numbers : 16

Spatial distribution of precipitation and streamflow and the results of field investigation

Year1950 1960 1970 1980 1990 2000

GD

P (b

illio

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an)

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Year1950 1960 1970 1980 1990 2000

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Year1950 1960 1970 1980 1990 2000

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Year1950 1960 1970 1980 1990 2000

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Changes in GDP(a), population (b), agriculture product (c), and livestock (d) within the Laohahe basin for the period of 1949–2005

1960 1970 1980 1990 2000

mm

/yea

r

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/yea

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0306090

120150

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/yea

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1960 1970 1980 1990 2000

degr

ee

56789

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Precipitation

Runoff

Pan Evaporation

Temperature

(d)

GW / P

Annual variations of five hydrological variables for the Laohahe basin during the period of 1956-2010

Yong, B., L. L. Ren, Y. Hong, et al. (2010), Hydrologic evaluation of Multisatellite Precipitation Analysis standard precipitation products in basins beyond its inclined latitude band: A case study in Laohahe basin, China, Water Resources Research, 46, W07542, doi:10.1029/2009WR008965.

Hydrologic Evaluation of TMPA –RT and TMPA-V6

Prec

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250Gauged PrecipitationRT 3B42 PrecipitationV6 3B42 Precipitation

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RT 3B42 vs. GaugeV6 3B42 vs. Gauge

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E (m

m)

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150RT 3B42 vs. GaugeV6 3B42 vs. Gauge

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Jan2002

Jan2003

Jan2004

Apr Jul Oct Jan2005

Apr Jul OctApr Jul Oct Apr Jul OctJan2000

Apr Jul Oct Jan2001

Apr Jul Oct

2002Jan

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2001Jan Apr Jul Oct Apr Jul OctApr Jul Oct Apr Jul OctApr Jul Oct Apr Jul Oct

Hydrologic error propagation Gauge validation

Now, TMPA mainly provides two kinds of precipitation productions (3-level):

1) The near real-time (i.e. TMPA-RT) product （available about 6-9 hours after real-time）;

2) The post-real-time research product (i.e. TMPA-P), which used about 6700 global rain gauges to adjust the satellite-derived precipitation, can be obtained about 10–15 days after the end of each calendar month.

Data can be downloaded from following website: ftp://trmmopen.gsfc.nasa.gov/pub/merged

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In China, many hydrological users adopted TMPA (lower biases) and CMORPH (higher correlation coefficient) data to drive hydrological models to simulate hydrological processes and predict flood at large scale basin, especially at the western China (ungauged basins).

It is the near-real-time availability that make TMPA-RT attractive for hydrological applications, such as flood monitoring and landslide detection. In Version 6 and current Version 7 of the TMPA real-time system, there was an important algorithm upgrade, i.e., climatological calibration algorithm (CCA), which occurred on 17 February 2009 and will be used in GPM-IMERGE algorithm.

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For hydrology, we are mainly concerned with 3 things related to satellite precipitation: 1) Resolution (higher resolution enough for mid- and large-sized basins); 2) Data quality and accuracy (suitable for simulation and prediction); 3) Real-time availability (within the routing concentration time for flood forecasting at basin scale)

You can see more details about CCA algorithm in: Yong, B., L.-L. Ren, Y. Hong, J. J. Gourley, Y. Tian, G. J. Huffman, X. Chen, W. Wang, and Y. Wen,2013: First evaluation of the climatological calibration algorithm in the real-time TMPA precipitation estimates over two basins at high and low latitudes, Water Resour. Res., 49, 2461-2472, doi: 10.1029/2009WR008965.

Evaluating the CCA algorithm from the hydrologic perspective

We used the best and independent CMWR gauge networks to evaluate the TMPA-RT before and after CCA.

To reduce the point-to-grid scale uncertainty, we only selected grid boxes that contained at least two gauges to evaluate.

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1) Compared to other seasons, the systematic error was notably higher during the winter in the Laohahe basin. This is because at high latitudes the frozen surfaces prevent retrievals with passive microwave window channels that respond to scattering. 2) Our assessment indicates that the CCA worked rather well for the low-latitude Mishui basin, having effectively reduced the error and bias of original TMPA-RT and improved its skill of detecting rainy events.

The CCA increased the RMSE values. This is only for daily scale. In real-time flood forecasting, we will use the 3 hourly datasets.

Hydrologic Potential of New CCA Algorithm in TMPA-RT

Intensity distributions before and after CCA

We found that the CCA changed the intensity distribution pattern of middle and high rain rates for both of the basins. In other words, the CCA improves on the long-term estimates of total precipitation amounts, at the price of altering the distribution features of rain rates.

Global Map of TMPA-RT vs TMPA-P

Figure 1: Global map of mean daily precipitation difference between (a) uncalibrated RTV6 data sets (RTV6_UC) and production V7, (b) calibrated RTV6 (RTV6_C) and production V7, (c) uncalibrated RTV7 (RTV7_C) and production V7, (d) calibrated RTV7 (RTV7_C) and production V7 for the three-year study period (from July 2008 to June 2011).

1. Generally, calibrated TMPA-RT data sets have lower relative biases than uncalibrated estimates (both Version 6 and Version 7) . e.g., Philippines, Indonesia, Southeastern China, and into Indian Ocean; Central Africa

RTV6_UC

RTV6_C

RTV7_C

RTV7_UC 2 Similar patterns were also found in Central America

3 But the Tibetan Plateau is an exception. The calibrated Version7 has a dramatic overestimation. Why?

Seasonal Variations

Figure 2: As in Fig. 1, but for seasonal variations: boreal (a-d) Spring (March-May), (e-h) Summer (June-August), (i-l) Autumn (September-November), and (m-p) Winter (December-February).

1. The main negative biases primarily occurred in the boreal warm season (JJA).

2 Overestimation in Central Africa mainly occurred in its local rainy seasons (MAM and SON). 3 Europe has negative biases in winter due to snow events.

4 Overestimation in central United States in summer due to the strong convective events.

Over land, almost all regions exhibit strong seasonality of bias.

Scan Frequency of Various Sensors

Figure 3: Global distribution of scan frequency of various microwave sensors introduced into the (left column) Version-6 and (right column) Version-7 TMPA real-time system: (a) and (b) No Observation; (c) and (d) Imagers; (e) and (f) Sounders; (g) and (h) IR.

Conical-scanning: TMI, AMSR, SSMI,SSMIS

Cross-tracking: AMSUI, MHS

Over the Tibetan Plateau, the usage of IR in Version-7 is more frequent than Version-6.

But the significant difference before and after climatological calibration might be attributed to the upward adjustment of CCA algorithm.

Scatterplots of daily TMPA-RT vs TMPA-P over land and ocean

Figure 4: Two-dimensional scatterplots of daily precipitation for (top) uncalibrated and (bottom)calibrated TMPA-RT against production V7 for (left two columns) land and (right two columns) ocean, corresponding to the maps in Fig. 1.

Latitudinal distribution of annual mean precipitation

Figure 5: Latitudinal distribution of the annual mean precipitation of four TMPA-RT estimates (RTV6_UC, RTV6_C, RTV7_UC, RTV7_C) and production V7 over both (a) land and (b) ocean.

Relative to land, the ocean looks more stable and smoother.

Statistics of Calibrated and Uncalibrated TMPA-RT v.s. TMPA-P

Table 1: Seasonal statistics of comparing daily accumulations of uncalibrated and calibrated TMPA-RT estimates (i.e., RTV6_UC, RTV7_UC, RTV6_C, and RTV7_C), taking daily V7 as the reference. Results are displayed for land, ocean, and global domains in the latitude band 50°N–50°S during the study period July 2008-June 2011. Shading indicates better statistics in each UC/C pair.

We can pretty much guarantee that the CCA made the real-time estimates statistically closer to the post-real-time product (but ground gauge observation?)

Using CPC Gauges to validate our results

Figure 6: Number of gauge stations in a 0.5° × 0.5° latitude-longitude grid for the CPC unified gauge-based analysis over the global land areas (from July 2008 to June 2011). The four selected validation regions (i.e., United States, East Asia, Europe, and Australia) are also shown.

Table 2: Seasonal statistics of comparing daily accumulations of uncalibrated and calibrated TMPA-RT estimates (i.e., RTV6_UC, RTV7_UC, RTV6_C, and RTV7_C), taking daily CPC estimates as the reference. Results are displayed for four densely gauged regions (i.e., United States, East Asia, Europe, and Australia) during the study period July 2008-June 2011. Shading indicates better statistics in each UC/C pair.

Statistics of Calibrated and Uncalibrated TMPA-RT v.s. CPC

The monthly CCA calibration tends to decease the TMPA’s systematic bias at long temporal scales, but increase the random errors at short duration. Such dynamic balance between systematic and random errors caused by the CCA leads to the increased RMSE values after climatological calibration.

This is only daily scale but not 3 hourly. I think the issue of 3 hourly will be more serious.

So if GPM developers hope more hydrological users used the IMERGE data in their applications, the uncalibrated pure satelllite estimates should be still remained in IMERGE data.

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Summary 1. As seen from the role of hydrology, the monthly climatological calibration algorithm (CCA) bias correction could homogenize the varying rainstorm characteristics, though it effectively reduced the systematic biases from the view of climatology.

2. As for flood prediction caused by rainstorm, it might be a better approach that uses sparse ground gauges to adjust the uncalibrated pure real-time TMPA or CMORPH data.

3. As for flood prediction over mid- or large-scale basins, the resolution should be less than 5km. So the 0.1 degree of GPM resolution seems still bigger. The latency time 3~4hr of GPM is OK for hydrological prediction at large scale.

Thank you for your attention!

Yong, B., D. Liu, J. J. Gourley, Y. Tian, G. J. Huffman, L. L. Ren, Y. Hong (2014), Global view of real-time TRMM Multi-satellite Precipitation Analysis: implication to its successor Global Precipitation Measurement mission, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-14-00017.1 (available Online).