# Presentation: 173862

## Comparison of TRMM climate rainfall products and gauge observations over Greece

**Objectives**

**Comparison of TRMM climate ****rainfall products ****and gauge observations over Greece**

***Haralambos Feidas******1******, ******Nektarios Chrysoulakis******2***
- 1Aristotle University of Thessaloniki, Department of Geology, Division of Meteorology-Climatology, Thessaloniki, Greece
- 2Foundation for Research and Technology - HELLAS, Institute of Applied and Computational Mathematics, Regional Analysis Division, Heraklion, Crete, Greece

- to perform an extensive **validation **and ** inter- comparison** of three TRMM climatic rainfall products, in mid-latitudes, on monthly-to-seasonal timescales,  using a dense network of 76 stations over Greece
- to test how the different satellite rainfall products **perform at the northernmost limit **of their spatial coverage.

**Data and Methodology**

- Monthly accumulated pre- cipitation values provided by the **three TRMM Level 3 standard products **(1998 – 2006)** **extending over a sufficient area of the Greek peninsula (from 35o to 41o):
- TMI precipitation (**3A12**),
- TRMM- Adjusted Merged Infrared Precipitation (**3B42**) analysis and
- TRMM and Other Sources Precipitation (**3B43**) analysis
- Monthly accumulated pre- cipitation measurements from a dense network of **76 rain gauges **over Greece was used to validate the satellite estimates.

**Results**** 1.**** *****Comparison on a monthly and seasonal basis***

**Area of study**

*The study area. *
*0.5° x 0.5° grid boxes with adequate gauge data for select satellite–**gauge **comparisons are also shown.*

- 1           2         3           4

***Interpolation*** ***scheme***: Rain gauge data were first **gridded** to a 0.5o lat/lon gridbox template using an **inverse distance weighted inter-polation **method which takes also into account the directional distribution of stations. Only boxes containing at least one gauge were used for the evaluation.
***Comparison methodology: ***Gauge data were compared with **monthly** and **seasonal** precipitation totals of the three satellite products using statistical scores such as *RMSE, Bias, MAE,  efficiency *score and* correlation coefficient.*

**Table 1.** Comparison statistics of monthly satellite rainfall estimates with gauge data on a 0.5° x 0.5° gridbox template for the mixed time-space domain (108 monthly totals x 46 to 56 grid cells)(period 1998 –2006).

**Results**** ****2****.**** *****Validation of the mean precipitation (1998-2006)***

**Figure 2.** Monthly values of error statistics for the three satellite rainfall products on a 0.5° x 0.5° gridbox template (period 1998 –2006).

**Figure 1.** Scatterplots of the three satellite rainfall products vs gauge data on a monthly basis for the mixed time-space domain (period 1998 –2006).

**Figure 3.** Seasonal values of error statistics for the three satellite rainfall products on a 0.5° x 0.5° gridbox template (period 1998 –2006).

- The **3A12** product has a very poor corres-pondence with gauge data (r=0.33). Errors are very large showing a considerable underestimation of precipitation, particu- larly at lower rainfall amounts (<150 mm).

- There are many grid boxes erroneously assigned a zero precipitation whereas for some grid cells the estimates are extremely overestimated.

**3b42** and **3B43** products show very good agreement with the ground reference (r > 0.8). The indirect use of gauge data induce a slight improvement in the 3B43 estimates, when compared with the 3B42, in reducing both the random errors and the scatter of the estimates.

- There is virtually no correspondence of **3A12** estimates with rain gauge data from January to June (r < 0.3). Correlation with reference data slightly increases during the rest of the year. Precipitation is strongly underestimated all over the year with the exception of the summer season. Random errors become extremely large in the dry summer months.

- The** 3B42** performance exhibits seasonal variation with the main rainy season (November to February) estimates showing the worst correlation with gauge data (r<0.7). Bias is small while random errors show an opposite seasonal variation with relatively

-  higher errors during the dry summer months.
- The **3B43** product exhibits an improved skill, especially in the main rainy season (November to March) although the bias error turned to positive values all over the year, indicating a systematic overestimation of precipitation.

**Figure 4.** Scatterplots of the three satellite rainfall products vs gauge data for the mean seasonal and annual precipitation (period 1998 –2006).

**Figure 5.** Difference fields (mm) and histograms of differences between the three satellite rainfall products and the gauge data for the mean annual precipitation (period 1998 –2006).

**Conclusions**

**3B43** shows an excellent performance for summer and autumn (r>0.92) which degrades in the winter, spring and annual precipitation. A reasonable agreement of the **3A12** with gauge data is evident only for the summer rainfall whereas for winter the correlation is zero.
- The strong underestimation of the mean annual precipitation by the **3A12** is noticeable in the difference field. The histogram of the **3B43** difference field clearly shows an increasing concentration of grid boxes with small differences comparing to the 3B42 and 3A12 products.

- This preliminary analysis has demonstrated that both the TRMM-adjusted merged infrared (3B42) and TRMM and other sources (3B43) rainfall products show very good agreement with gauge data over Greece on monthly-to-seasonal timescales and 0.5o space scales. The best correspondence with reference data is obtained for summer and autumn, although the minimal summer rainfall results in higher relative errors.
- The 3B43 has shown slight improvement, when compared with the 3B42, in reducing both the random errors and the scatter of the estimates, although a systematic overestimation of the precipitation is introduced.
- The TRMM TMI (3A12) data present a very poor performance over Greece probably as a result of undersampling and algorithm inconsistencies over coastal areas.