# Presentation: 947104

## Use of a dense rain gauge network over India for improving blended TRMM products and downscaled weather models

- Department of Meteorology, Florida State University,
- Tallahassee, FL 32306, USA.

**T. N. Krishnamurti and Akhilesh K Mishra**

***The 3rd International TRMM Science Conference*****, at Bally's Hotel in Las Vegas, NV Feb. 4-8, 2008**

## 
This work required the following tools:

High resolution rainfall observations
High resolution NWP model forecasts
Physical Initialization
Statistical Downscaling
FSU Multimodel Superensemble
Skill Score Metrics 

**This work required the following tools:**
**High resolution rainfall observations**
**High resolution NWP model forecasts**
**Physical Initialization**
**Statistical Downscaling**
**FSU Multimodel Superensemble**
**Skill Score Metrics **

**Tools Required**

## Outline

**Conventional ****superensemble**** methodology**
**All India ****High Resolution**** gridded rainfall**
**Multimodel ****Downscaling****: NWP Experiment**
**Downscaling Procedure**
**Models used**
**Results:**
**Spatial Patterns of Precipitation for Day 1 and Day 5 of forecasts**
**Equitable Threat Scores**
**Conclusions and Future work**

##  In addition to removing the bias, the superensemble scales the individual model forecasts contributions according to their relative performance in the training period in a way that, mathematically, is equivalent to weighting them.

-  In addition to removing the bias, the superensemble scales the individual model forecasts contributions according to their relative performance in the training period in a way that, mathematically, is equivalent to weighting them.

## High Resolution gridded  all India daily observed  rainfall

- New High resolution gridded IMD rainfall data  (Rajeevan et al. 2005, 2006 ) based on  a dense network of daily raingauge observations were available to us. Daily files valid at 03Z GMT were prepared by National Climate Date Center, IMD Pune and were made available to us by Dr. Rajeevan and his team. There are 2 versions of this data set, older version was based on 1803 raingauge (v. 1) stations while version 2 is prepared using 2140 stations.

## Future Rain gauge Network  

**Dr. Akiyo Yatagai (JAPAN)****‏**

## TRMM-3B42RT and IMD ObservationFSU-TRMM Algorithm   

- Most of the operational models which are part of our multimodel suite were available at 12Z GMT. Daily forecasts are stored at 12Z UTC. It was necessary to construct  the superensemble using 03Z observed daily IMD rain.
- In order to bring in the diurnal cycle and to correct for the aforementioned temporal discrepancy between the model and IMD rain it became necessary to use diurnal weights using TRMM-3B42 precipitation files that were available on 3 hourly intervals.
- IMD rain at 12Z is then regressed over a period of 90 days (i.e. June to August 2007) against the TRMM 3B42RT daily total at 12Z.
- Regression coefficients ‘a’ and ‘b’ were used to get new blended TRMM rain.

## Average Rain for June (Top three) and July (Bottom three) 2007 compared TRMM-3B42, IMD and FSU-TRMM Algorithm 

## Average Rain for Aug. (Top three) and Sep. (Bottom three) 2007 compared TRMM-3B42, IMD and FSU-TRMM Algorithm  

## Member Models of FSU multimodel suite

- Member Models used in this study are
**GFS model, National Centers for Environmental Prediction, USA (NCEP)**
**JMA Global Spectral Model, Japan Meteorological Agency (JMA)**
**ECMWF model, European Centre for Medium-Range Weather Forecasts **
**GASP model, Australian Bureau of Meteorology (BMRC)**

## Statistical Downscaling 

- Following are the main steps in the downscaling
**Step 1: Preparation of new observed rain valid at 12 Z GMT, using IMD rain gauge based high resolution data and TRMM (3B42RT) 3 hourly products****.**
**Step 2: Interpolation of Large scale models at the IMD grids (i.e. 0.5 x 0.5 degree). We used simple linear regression method to get the scaling coefficients. **
**	****	**** ****	****Y = ****	****aX ****	****+ ****	****b**
**        ****where Y is Observed high resolution rain based on the IMD data sets and X is model interpolated rain, where  ‘a’ and ‘b’ are the regression coefficnets. These coefficients are calculated for each member model and each day of forecast separately. **
**	**** ****90 days of data from observations and from member model forecasts were used to determine the downscaling coefficients **
**Step 3: Using these regression coefficients we prepared a downscaled high resolution precipitation for each of the member models. These forecasts are then used for constructing the new downscaled Superensemble (at 0.5 degree lat/lon resolution).**** **

## Following terms will be used frequently throughout the presentation

- Interpolated: Large scale forecast interpolation using bilinear method to the IMD grid (0.5 x 0.5 degree)‏
- Downscaled: Forecast prepared using the regression coeffcients ‘a’ and ‘b’.
- FSU-TRMM Algorithm: Rainfall derived using TRMM-3B42RT and IMD observations.
- Member Models:
**NCEP**
**ECMWF**
**BMRC**
**JMA**

## Regression Coefficients ‘a’ and ‘b’ for ECMWF

**‘****a’**
**‘****b’**

**High positive values of  ‘a’ (a>1) denote model underestimating the rain, while low positive values (0<a<1) represent model over predicting. Negative values of ‘a’ denote that model overpredicts for lower values of observed precipitation and underpredicts for higher values of observed precipitations.**
**Preponderances of red color (high / positive value) for the coefficient ‘b’ denotes large systematic error in the  model and vice versa.**
** **

## Interpolated and Downscaled Spatial Correlation for all India domain:  Time series for Day 1 forecast 

## Interpolated and Downscaled Spatial Correlation for all India domain:  Time series for Day 5 forecast

## Interpolated and Downscaled Compared with Observed (FSU-TRMM Algorithm) 1st July 2007 : Day 1 of forecasts

## Interpolated and Downscaled Compared with Observed (FSU-TRMM Algorithm) Seasonal Average Day 1 forecast (June to August 2007)‏

- Interpolated and Downscaled Compared with Observed (FSU-TRMM Algorithm)** **Day 5 forecast valid at: 12Z 23rd June 2007

**Seasonal Average of Day 5 forecasts ****(June to August, 2007)****‏**

## ETS skills for Member ModelsForecasts from Day 1 through Day 5

**NCEP**

**ECMWF**

**Black dotted lines****: Large Scale Interpolated Models ETS **
**Red Lines****: Downscaled Model’s ETS**

**Threshold (mm/day)****‏**

**Threshold (mm/day)****‏**

## ETS skills for Member ModelsForecasts from Day 1 through Day 5

**BMRC**

**JMA**

**Black dotted lines****: Large Scale Interpolated Models ETS **
**Red Lines****: Downscaled Model’s ETS**

**Threshold (mm/day)****‏**

**Threshold (mm/day)****‏**

## Multimodel Downscaled Superensemble

**Using the June to August daily observed rain and downscaled forecasts from the multimodel   Superensemble with 90 days training is prepared. **
**Superensemble forecasts were possible only for month of September (i.e. September 1****st**** to 30****th**** 2007).**
**Some Case Studies showing examples**

## Conventional (without downscaling) Multimodels Skills

## Probabilistic Skills for Conventional (without downscaling)  Multimodel suite

## Probabilistic Skills for Conventional (without downscaling) Multimodel suite

##  Multimodel (Downscaled) suite Skillsof  multimodel Superensemble Suite

## Probabilistic Skills for Downscaled Multimodel suite: Summery of 30 forecasts

## Probabilistic Skills for Downscaled Multimodel suite: Summery of 30 forecasts 

## FSU TRMM Algorithm based SuperensembleDay 1 forecast: Multimodels valid for September 3rd 2007

## FSU TRMM Algorithm based SuperensembleDay 1 forecast: Multimodels valid for September 5th 2007

## FSU TRMM Algorithm based SuperensembleDay 1 forecast: Multimodels valid for September 7th 2007

## FSU TRMM Algorithm based SuperensembleDay 5 forecast: Multimodels valid for September 7th 2007

## FSU TRMM Algorithm based SuperensembleDay 5 forecast: Multimodels valid for September 9th 2007

## FSU TRMM Algorithm based SuperensembleDay 5 forecast: Multimodels valid for September 24th 2007

## Comparing different Superensemble’s 

**We will now compare only Superensemble forecast (prepared with different observation data sets) probabilistic skills against each other. Here are the four Superensemble runs.**
**Superensemble with Interpolated models against TRMM-3B42RT, **
**Superensemble with downscaled models against TRMM-3B42RT****, **
**Superensemble with downscaled models against IMD Rain,**** **
**Superensemble with downscaled models against FSU-TRMM Algorithm.**

## Probabilistic Skills comparison for different SuperensembleInterpolated, Downscaled against TRMM, Downscaled against IMD and Downscaled against FSU-TRMM Algorithm

## Probabilistic Skills comparison for different SuperensembleInterpolated, Downscaled against TRMM, Downscaled against IMD and Downscaled against FSU-TRMM Algorithm

## Conclusions

- The main findings of this study are:
- FSU-TRMM algorithm based rain is close to the IMD (2140 rain gauge) based rain over land areas of India.
- Use of a statistical downscaling and a superensemble methodology for multimodel forecasts of weather and seasonal climate provide a major improvement for prediction at high resolution.
- The availability of a dense rain-gauge network was essential for the success of this work. The NWP products we produce are at a resolution of 0.5° x 0.5° latitude/longitude.

## Conclusions

- The present methodology for weather and seasonal climate provides superior forecasts compared to the followings:
- Conventional multimodel superensemble based on forecasts from large scale models.
- Forecasts made by individual member models such as ECMWF, NCEP(EMC), JMA and BMRC.
- Forecast based on bias removed member model products and ensemble mean.
- Preliminary studies show that skills comparable to our current downscaled superensemble are not attainable using a suite of high resolution mesoscale models.
- Has great benefit for real time.
- Future work over Eurasia, North America and Brazil would be possible (**3K algorithm**)‏
- It would be possible to do most of this work at rain gauge sites rather than grid points (i.e. site specific TRMM algorithm, downscaling and superensemble).

- Thank You