combining cmorph and rain gauges observations over the...

Alejandra De Vera - Rafael Terra

Combining CMORPH and Rain Gauges Observations over the Rio Negro Basin

Proyecto Piloto de Alerta Temprana para la Ciudad de Durazno ante las Avenidas del río Yí PROYECTO PROHIMET – YÍ / OMM-FJR-IMFIA

Framework


Part of the work was conducted in the framework of a PROHIMET-WMO project: “Proyecto Piloto de Alerta Temprana para la Ciudad de Durazno ante las Avenidas del río Yí”. I enjoyed a research initiation fellowship from ANII that greatly contributed to this work.

Objective


Describe and evaluate several methodologies for merging CMORPH satellite precipitation estimate and daily gauge data at a basin scale with a high density of surface observations and a seven-year long record for comparison.

Our objective is not to evaluate the CMORPH estimate, but to compare several ways of using CMORPH to improve estimates of precipitation when used in combination with gauge data.

We evaluate the combined product (with the different methodologies) at points rather than area averages because point precipitation is know with more certainty, which makes the determination of skill more robust.

Methodology


Datasets.

Skill scores.

Ability of the existing rain gauge network to estimate the precipitation at a generic point.

Methodologies to remove the CMORPH bias.

Evaluation of the incremental skill of combined precipitation estimates is discussed.

Conclusions.

Precipitation data comes from UTE network, composed of 133 rain gauges within the Rio Negro basin.

The satellite-based algorithm used is CMORPH, at a 3-hour frequency and a spatial resolution of 0.25° x 0.25°. However, the study is limited to daily precipitation totals, since this is the information available at the rain gauges.

The period analyzed is from 2003 to 2009 (during which both datasets are available).

Datasets


Rain gauge network and the CMORPH grid in the Rio Negro basin


Rio Negro basin, with an area of 71,000 km2, is one of the largest watersheds in Uruguay and plays a key role in the hydroenergy production of the country. For this reason, the precipitation station density is relatively high with a characteristic separation between gauges of 23 km.

108 CMORPH points

133 rain gauges

We determine the performance of the combined precipitation estimates based on:

the bias score (BIAS),

the probability of detection (POD),

the false alarm ratio (FAR),

the Equitable Threat Score (ETS).

Each score was computed for daily precipitation thresholds ranging from 0 to 100 mm, every 5 mm.

Also, we computed other parameters:

the root mean square error (RMSE),

the linear correlation coefficient.

Skill scores


As a reference, we first compute the ability of the existing rain gauge network to estimate the precipitation at a generic point by determining the unbiased probability of detection of daily precipitation as a function of distance and rainfall amounts. For each pair of stations, the unbiased relative POD was computed; bias was removed following the quantile matching technique (presented below). POD scores were sorted by the distance among the stations involved and then averaged in 10 km bins to construct the contour plots. Separate analyses were performed for the April-September semester (cold season) and October-March semester (warm season).

Ability of the rain gauge network


0.1

0.1

0.1

0.1

0.2

0.2

0.2

0.2

0.3

0.3

0.3

0.3

0.4

0.4

0.4

0.4

0.5

0.5

0.5

0.5 0.50.6

0.6

0.7

0.8D

ista

nce (

km

)

Daily rain threshold (mm)

Warm Season

0 10 20 30 40 50 60 70 80 90 100

50

100

150

200

250

3000.1

0.1

0.2

0.2

0.2

0.3

0.3

0.3

0.3

0.4

0.4

0.4

0.4

0.5

0.5

0.5

0.5

0.5

0.6 0.6

0.6

0.6

0.6 0.60.7

0.7

0.8

Dis

tan

ce (

km

)


Cold Season

0 10 20 30 40 50 60 70 80 90 100

50

100

150

200

250

300

We observe slightly higher levels of detection for a given distance in the cold season, when frontal systems are dominant, than in warm season, when smaller scale convective systems are more frequent. This is most evident for large thresholds.

POD as a function of distance and daily rain threshold for cold and warm seasons - Rain gauge network



Ability of the rain gauge network

These results summarize the present ability of the rain gauge network alone to estimate the precipitation at a generic point in the basin.

We next explore the ability of the satellite retrievals to improve the skill when used in combination with gauge data.

We devised several bias removal schemes from which we developed three methodologies to combine the satellite retrievals with rain gauge data.

(1) Historical quantile matching (Requires historical information on nearby rain gauges)

(2) Adjustment based on simultaneous nearby stations (Requires simultaneous observations on nearby rain gauges)

(3) Quantile matching + adjustment (Requieres both historical information and simultaneous

observations on nearby rain gauges)

Bias removal schemes


(1) A quantile-matching algorithm that removes the bias in the distribution of daily precipitation intensity forcing the distribution of the estimator to coincide with the historical distribution at a nearby rain gauge;

(2) An adjustment that makes use of simultaneous nearby surface observations to estimate CMORPH deviations, which is then interpolated to the target point and removed;

(3) A method that combines the two previous ones: first applies the quantile-matching of CMORPH estimates and then adjusts through interpolation of the remaining differences between CMORPH and the neighboring gauges.



(1) Historical quantile matching First, every CMORPH estimate during the period is quantile

matched on its own grid using the historical information from the nearest rain gauge.

The matched estimations are then interpolated from CMORPH grid to the stations and compared against actual observations to compute scores.



(2) Adjustment based on simultaneous nearby stations First, CMORPH estimates are interpolated to each rain gauge

and the difference is computed at said points by subtracting the simultaneously observed values.

Withdrawing one station at a time in a cross validation approach, these differences are further interpolated from all remaining gauges and used to adjust the interpolated CMORPH value at the withdrawn station.

Finally, the adjusted estimate is compared against the actual observation to compute scores.



Note: Systematic biases are not guaranteed to be eliminated through this adjustment, as was the case with quantile matching. However, later results will show that the CMOPRH estimation adjusted in this way presents very small biases.

(3) Quantile matching + adjustment First, every CMORPH estimate is quantile matched on its own

grid.

Second, matched CMORPH estimates are interpolated to each station and the difference is computed at said points by subtracting the simultaneously observed values.

Withdrawing one station at a time, these differences are further interpolated from all remaining gauges and used to adjust the interpolated matched CMORPH value at the withdrawn station.

Finally, the matched adjusted estimate is compared against actual observation to compute scores.



Average and standard deviation of BIAS and POD - Quantile matched, adjusted and matched-adjusted CMORPH with linear interpolation

The adjustment based on simultaneous nearby stations greatly improves the POD as compared with quantile matched alone, without a significant detrimental effect on the bias. Within the adjusted estimates, the impact of a previous quantile matching is extremely small in both scores.


0 10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2

2.5

Daily Rain Thresholds (mm)

BIA

S

Average BIAS in the Rio Negro basin / Period 2003-2009

Quantile matched CMORPH

Adjusted CMORPH

Matched-adjusted CMORPH

Incremental skill of combined precipitation estimates

0 10 20 30 40 50 60 70 80 90 1000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Daily Rain Thresholds (mm)

PO

D

Average POD in the Rio Negro basin / Period 2003-2009

Quantile matched CMORPH

Adjusted CMORPH

Matched-adjusted CMORPH

Differences in POD between the matched CMORPH estimation and the rain gauge network as a function of distance and daily rain threshold

The quantile matched CMORPH has a higher POD -compared with that obtained from surface observations only- for distances greater than approximately 50 km, but not in the case of Rio Negro basin where the characteristic distance between gauges is 23 km.



-0.1-0.1 -0.1

0

0

0 0

0.1

0.1

0.1

0.1

0.2 0

.2

0.2

0.2

0.2 0.2

0.3

0.3

0.3

0.3

0.3

0.4

0.4

0.4

0.4

Dis

tan

ce (

km

)


POD Difference: Matched CMORPH - Rain Gauges / Cold Season

0 10 20 30 40 50 60 70 80 90 100

50

100

150

200

250

300

-0.2 -0.2 -0.2-0.1 -0.1 -0.10

00

0

0.1

0.1

0.1

0.1

0.1

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.3

0.3

0.3

Dis

tan

ce (

km

)


POD Difference: Matched CMORPH - Rain Gauges / Warm Season

0 10 20 30 40 50 60 70 80 90 100

50

100

150

200

250

300

Cold Season Warm Season

POD as a function of distance and daily rain threshold for cold and warm seasons – Linearly interpolated adjusted CMORPH



0.4

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.6

0.6

0.60.6

0.6

0.7

0.7

0.7 0.70.70.8

0.8

0.80.9

Dis

tan

ce (

km

)


POD as a function of distance and daily rain threshold

Interpolation from adjusted CMORPH - Cold Season

0 10 20 30 40 50 60 70 80 90 100

50

100

150

200

250

300

0.2

0.2

0.2

0.3

0.3

0.3

0.3

0.3

0.3

0.4

0.4

0.4

0.40.4

0.5

0.5

0.5

0.5 0.5

0.6

0.6

0.6

0.6 0.6

0.7

0.7

0.7

0.7 0.7 0.70.8

0.8

0.9

Dis

tan

ce (

km

)


POD as a function of distance and daily rain threshold

Interpolation from adjusted CMORPH - Warm Season

0 10 20 30 40 50 60 70 80 90 100

50

100

150

200

250

300


Differences in POD between the linearly interpolated adjusted CMORPH estimation and the rain gauge network as a function of distance and daily rain

The adjusted estimate performs better, as measured by POD, than using only surface data, for the entire range of distances and daily precipitation thresholds and for both seasons.



00

0.1 0.10.1 0.1

0.1

0.1

0.1

0.1

0.1

0.2

0.2

0.2

0.20.2

0.2

0.2

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

Dis

tan

ce (

km

)


POD Difference: Adjusted CMORPH - Rain Gauges / Cold Season

0 10 20 30 40 50 60 70 80 90 100

50

100

150

200

250

300

0.10.1 0.1

0.1

0.1 0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.2

0.2

0.2

0.2

0.2

0.2 0.2

0.20.3

0.30.3

0.3

0.3

Dis

tan

ce (

km

)


POD Difference: Adjusted CMORPH - Rain Gauges / Warm Season

0 10 20 30 40 50 60 70 80 90 100

50

100

150

200

250

300


Conclusions


The adjustment based on simultaneous nearby stations (methodology 2) greatly improves the POD as compared with quantile matched alone (methodology 1), without a significant detrimental effect on the bias.

Within the adjusted estimates, the impact of a previous quantile matching is extremely small in both the bias and POD scores.

Considering the extra requirement in historical data, its use is not justified. However, in the absence of simultaneous nearby observations with which to compute the adjustment, the historical quantile matching is still necessary and useful to remove the bias.

Conclusions


The incremental skill attained in the representation of the precipitation field through the appropriate addition of satellite retrievals to the rain gauge data is already measurable, although not very significant when the network density is high as in the present study.

It is reasonable to expect that the satellite products continue to improve over time, providing an encouraging scenario for the combined use of satellite technology and rain gauges in the determination of rainfall distribution.


Any suggestions?

Thanks!

combining cmorph and rain gauges observations over the...

Documents