Error analysis of satellite rainfall products over complex terrain basins Yiwen Mei & Emmanouil N. Anagnostou

STUDY AREA: Upper Adige River Basins

Methodology and Error Metrics

• Totally 3249 rainfall events pairs (rainfall

accumulation, RV, greater than 3 mm);

• Basin scale: small scale and medium scale

(threshold area at 530 km2);

• Seasonality: convective storm from May –

Aug and frontal storm from Sep – Dec;

• RV quantile ranges: [50% 80%), [80%

90%), [90% 95%) and [95% 100%].

landslides, and debris flow, triggered by heavy precipitation events (HPEs). In situ observations over

mountainous areas are limited, but currently available satellite precipitation products can potentially

provide the precipitation estimation needed for hydrological applications. In this study, four widely

used satellite-based precipitation products [Tropical Rainfall Measuring Mission Multisatellite

Precipitation Analysis (TMPA) 3B42 version 7 (3B42-V7) and in near–real time (3B42-RT), Climate

Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely

Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their

performance in capturing the properties of HPEs over different basin scales.

Boxplot of Relative Centroid Displacement

Boxplot of Multiplicative Error

• Higher CC and lower RMSE values for the two

3B42 products;

• Significant underestimation from CMORPH and

PERSIANN (particularly in the cold period).

• ε runs from over to underestimation with increase in quantile range, particularly apparent for the CMORPH and PERSIANN;

• The value range of ε decreases with increasing quantile values and basin scale.

AI-based Score for HPEs

Boxplots of Score for HPEs

Scale

Class

Area

(km2)

Elevation (m.a.s.l) Num. of

Gauges mean STD

S1 208 2040 606 12

S2 236 1859 401 8

S3 255 1894 448 11

S4 345 1884 691 13

S5 391 1892 516 11

S6 417 1598 557 13

S7 427 1770 744 16

S8 505 2008 618 22

M1 1262 1979 673 25

M2 1906 1958 687 33

M3 1992 1951 690 35

M4 2863 1904 758 55

M5 4166 1770 838 71

0.25°satellite grid

• Accuracy Index (AI, 0 to 1):

𝑑𝑐 =𝑡𝑐,𝑠 − 𝑡𝑐,𝑔

𝐷𝑔

• Multiplicative Error (ε):

𝜀 =𝐼𝑆𝐼𝐺

𝐴𝐼 =2 𝐼𝑆𝐼𝐺𝐼𝑆 + 𝐼𝐺

• Relative Centroid Displacement (dc):

Rainfall Events Classification

Error Metrics

ε > 1, overestimation

ε < 1, underestimation

AI = 0, Missing/false alarm

AI = 1, Perfect Match

dc > 0, delay in arrival

dc < 0, advance in arrival

Where IS (IG) is the interested properties of

rainfall events (i.e. event duration D, rainfall

accumulation RV, and maximum rainfall rate RM).

• Score system (S, 0 to 1):

Scatterplot of Multiplicative Error for HPEs

• No clear trend in either delay or advance in detection over the various compared dimensions for different satellite products;

• Shorter duration events (from the small scale and lower quantile range) have larger variability in dc values.

S = 0, Missing/false alarm

S = 1, Perfect Match

𝑆 =𝐴𝐼𝐷𝐴𝐼𝑉

3

+𝐴𝐼𝑀𝐴𝐼𝐷

3

+𝐴𝐼𝑉𝐴𝐼𝑀

3

Periods Statistics 3B42-RT 3B42-V7 CMORPH PERSIANN

Warm CC 0.38 0.51 0.06 0.00

RMSE 0.66 0.38 0.57 0.98

Cold CC 0.34 0.04 0.11 0.06

RMSE 0.72 0.54 0.86 0.80

Summary: Accurate quantitative precipitation estimation over mountainous basins is

• Resolution of the satellite data:

0.25°/3-hourly;

• Resolution of rain gauge data:

points/hourly;

• Rainfall records from 2003 to 2010

• High AI values (greater than 0.9) for heavy precipitation events over the

summer months;

• Slightly decrease in AI from medium to small scale and warm to cold period;

• Relatively low AI values for RV and RM from CMORPH and PERSIANN

estimation over the cold period;

• High consistency in estimating D for the various compared dimensions.

• Values S for 3B42-V7 and 3B42-RT are

concentrated near unity;

• Higher degree of centralization for larger

size basins;

• 3B42-V7 is the best product in most of the

compared dimensions;

Limitations and Future Works

• The results represent a limited hydroclimatic and geomorphologic regime;

• S values of CMORPH and PERSIANN are low with high variability.

of great importance because of their susceptibility to hazards such as flash floods, shallow

• Existences of snow-covered surfaces and mixed-phase

precipitation during the late phase of cold period;

• Evaluate the error propagation in simulating the

hydrologic response over mountainous basins for

coarse and high spatial resolution satellite products.