bias-correction and dynamical downscaling strategy to

ISSN 0252-1075

Contribution from IITM

Research Report No. RR-145

ESSO/IITM/SERP/SR/01(2019)/196

Bias-Correction and Dynamical Downscaling Strategy to Improve

the Prediction of Extreme Weather Events on Extended Range

Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay

Indian Institute of Tropical Meteorology (IITM) Earth System Science Organization (ESSO)

Ministry of Earth Sciences (MoES) PUNE, INDIA

http://www.tropmet.res.in/

ISSN 0252-1075

Contribution from IITM

Research Report No. RR-145


Bias-Correction and Dynamical Downscaling Strategy to Improve

the Prediction of Extreme Weather Events on Extended Range

Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and

R. Chattopadhyay

*Corresponding Author Address:

Dr. A.K. Sahai

Indian Institute of Tropical Meteorology,

Dr. Homi Bhabha Road, Pashan,

Pune – 411008, India.

E-mail: [email protected]

Phone: +91-(0)20-25904520

Indian Institute of Tropical Meteorology (IITM)

Earth System Science Organization (ESSO)

Ministry of Earth Sciences (MoES)

PUNE, INDIA

http://www.tropmet.res.in/

DOCUMENT CONTROL SHEET --------------------------------------------------------------------------------------------------------------------------------------

Earth System Science Organization (ESSO) Ministry of Earth Sciences (MoES)

Indian Institute of Tropical Meteorology (IITM)

ESSO Document Number


Title of the Report Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather

Events on Extended Range

Authors Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay

Type of Document

Scientific Report (Research Report)

Number of pages and figures

37, 17

Number of references

37

Keywords Dynamical downscaling, Bias-correction, Extended range prediction

Security classification Open

Distribution

Unrestricted

Date of Publication

January 2019

Abstract

This study aims to document the strengths and shortcomings of forecast strategy being developed

based on dynamical downscaling for better extended range prediction of extreme weather events.

One heavy rainfall event and two cyclone cases are selected to dynamically downscale the global

forecasts of Extended Range Prediction (ERP) system using Weather Research and forecasting

(WRF) model at 9km for Indian domain. The ERP outputs are bias-corrected and fed to WRF as

lateral boundary conditions to minimize migrated errors from parent model via boundary

conditions. Results show bias-corrected downscaled ERP is more efficient in predicting extreme

weather with 10-12 days lead time.

Summary

Indian subcontinent is prone to weather extremes like cyclones and heavy rainfall events causing

severe damages due to floods and landslides. General circulation models (GCMs) running at coarse

resolution are unable to forewarn the intensity of these catastrophic rains at sufficient lead time. In

this study, a forecast strategy of dynamical downscaling is adopted to improve the extended range

predictions of high impact weather events. Ensemble members of an Extended Range Prediction

(raw-ERP) system based on higher (T382) and lower (T126) resolution of Climate Forecast System

(CFSv2) and Global Forecast system (GFS) are downscaled individually using Weather Research and

Forecasting (WRF) model. WRF run at 9 km resolution for Indian region such that the selected

model domain is large enough to include ocean-atmospheric interactions in lateral boundary

conditions. Downscaled ERP (ERPWRF) will take advantage of good prediction skills of raw-ERP in

capturing the large scale signals and will help to reduce spatio-temporal errors in regional detailing.

For the present study we have selected three different events - two cyclone cases and one heavy

rainfall event. Global raw-ERP ensemble forecasts are bias-corrected to minimize GCM intrigued

biases via boundary conditions. Bias-corrected and downscaled forecasts show improvement in

predicting the spatio-temporal evolution of rainfall associated with the selected severe weather

cases. This study affirms that dynamical downscaling can be an effective tool to generate useful

high resolution predictions at 10-12 days lead time.

Contents

Abstract 1

Summary 2

1 Introduction 3

2 Data and Methodology

2.1 Models

2.2 Bias-correction & dynamical downscaling

2.3 Objective tracking algorithm

5

5

5

6

3 Results and Discussions

3.1 Cyclonic Storm Roanu of 2016

3.1.1 Results from Experiments

3.1.2 Results from final setup

3.2 Uttarakhand Heavy Rainfall 2013

3.3 Severe Cyclonic Storm Mora of 2017

6

6

7

8

8

9

4 Conclusions and Remarks 10

Acknowledgements 11

References 12

Figures 15

1

Bias-Correction and Dynamical Downscaling Strategy to

Improve the Prediction of Extreme Weather Events on

Extended Range

Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and

R. Chattopadhyay

Indian Institute of Tropical Meteorology, Pune-411008

Abstract

This study aims to document the strengths and shortcomings of forecast strategy being developed based

on dynamical downscaling for better extended range prediction of extreme weather events. One heavy

rainfall event and two cyclone cases are selected to dynamically downscale the global forecasts of

Extended Range Prediction (ERP) system using Weather Research and forecasting (WRF) model at 9km

for Indian domain. The ERP outputs are bias-corrected and fed to WRF as lateral boundary conditions to

minimize migrated errors from parent model via boundary conditions. Results show bias-corrected

downscaled ERP is more efficient in predicting extreme weather with 10-12 days lead time.

*Corresponding Author:

Dr. A.K. Sahai

Indian Institute of Tropical Meteorology,

Dr. Homi Bhabha Road, Pashan,

Pune – 411008, India.

E-mail: [email protected]

Phone: +91-(0)20-25904520

2

Summary

Indian subcontinent is prone to weather extremes like cyclones and heavy rainfall events causing severe

damages due to floods and landslides. General circulation models (GCMs) running at coarse resolution

are unable to forewarn the intensity of these catastrophic rains at sufficient lead time. In this study, a

forecast strategy of dynamical downscaling is adopted to improve the extended range predictions of high

impact weather events. Ensemble members of an Extended Range Prediction (raw-ERP) system based on

higher (T382) and lower (T126) resolution of Climate Forecast System(CFSv2) and Global Forecast

system (GFS) are downscaled individually using Weather Research and Forecasting (WRF) model. WRF

run at 9 km resolution for Indian region such that the selected model domain is large enough to include

ocean-atmospheric interactions in lateral boundary conditions. Downscaled ERP (ERPWRF) will take

advantage of good prediction skills of raw-ERP in capturing the large scale signals and will help to reduce

spatio-temporal errors in regional detailing. For the present study we have selected three different events -

two cyclone cases and one heavy rainfall event. Global raw-ERP ensemble forecasts are bias-corrected to

minimize GCM intrigued biases via boundary conditions. Bias-corrected and downscaled forecasts show

improvement in predicting the spatio-temporal evolution of rainfall associated with the selected severe

weather cases. This study affirms that dynamical downscaling can be an effective tool to generate useful

high resolution predictions at 10-12 days lead time.

Key-Words: Dynamical downscaling, Bias-correction, Extended range prediction

3

1. Introduction

Extreme weather events are devastative due to their impacts on society such as excessive loss of lives,

severe economic collapse and endangered ecosystems. Cyclonic storms and low pressure systems causing

exceptionally heavy precipitation are few examples of extreme events. Studies have shown increasing

trends of heavy rainfall( ≥ 100 mm/day) and very heavy rainfall (≥150 mm/day) over Indian region1.

Increased frequency of such severe weather events are suspected to be linked to changes in hydro-cycle

due to enhanced green-house effect2. Forewarning of these extreme cases at least 10-12 days in advance

can help in reducing socio-economic loss.

The forecasting of meteorological parameters with 2-3 weeks lead time is referred as extended range

prediction. This sub seasonal forecast range has predictability due to the presence of quasi-periodicities in

intra-seasonal scales; and is important for different sectors like agriculture, health, water management etc.

A real time forecast system for extended range prediction (ERP) of active and break spells of Indian

summer monsoon is developed at Indian Institute of Tropical Meteorology (IITM). This system is now

operational at India Meteorological Department (hereafter termed as IITM/IMD ERP). ERP system is a

combination of deterministic as well as probabilistic forecast which includes coupled model Climate

Forecast System (CFSv2)3 and Atmospheric model Global Forecast System (GFS) at higher (T382) and

lower (T126) resolutions with ensembles of perturbed initial conditions (ICs)4.

ERP has impressive skill in predicting Monsoon onset & progression, cyclone genesis, Madden-Julian

Oscillation, heat and cold waves with a lead time of two weeks5. It has been found in previous studies that

ERP system is successful in providing outlooks on impending heavy rainfall events, albeit there are some

spatio-temporal errors5,6

. Therefore, it is hypothesized in this study that if the model could reasonably

predict the large scale features associated with heavy rainfall events, then it is possible to reduce the

spatio-temporal errors using dynamical downscaling approach. Skillful prediction of extreme events is

needed at sufficient lead time and since the IITM/IMD ERP system can capture the large scale features

well, downscaling of ERP outputs will be an added value.

Dynamical downscaling involves running high resolution model (generally regional models (RCMs)) at

regional sub-domain with coarser resolution model outputs as large scale boundary conditions.

Traditional way of such downscaling is using global model outputs directly, which is likely to infuse

GCM biases via initial and lateral boundary conditions7. The biases in parent GCM when given as initial

and lateral boundary conditions (hereafter referred as ICs and LBCs) are likely to grow further in RCM

simulations and the results can have too much deviation with time. Analyzing the climate change

4

simulations within ENSEMBLES project, Christensen suggested that a simple bias-correction that can

retain synoptic and climate variability signals8 should be applied to control error growth. Dynamical

downscaling with such bias-corrections is found to be effective in different climate projections. There are

studies with RCM that show bias-corrected winds and sea-surface temperature (SST) alleviate shear bias,

and bias correction of all boundary variables helps in cyclone development in Atlantic9. Bias-corrections

applied to RCM input variable like wind speed, temperature, geo-potential height, specific humidity and

SST shows better simulated rainfall intensity over Mongolia10

. A study over central U.S. and Canada

illustrates that bias-correction can significantly improve downscaled middle-upper air temperature, wind

vectors, geo-potential and water vapor7. Aforementioned studies based on dynamical downscaling deals

with climate simulations and future projection in general, with some studies addressing individual

weather extreme cases in particular8,9,11–14

. Novelty of the present work is that it downscales global

extended range predictions in forecasting mode using Weather Research Forecast (WRF). The 16 ERP

ensemble members are given as LBCs to the WRF which have signals of MJOs and ISOs15

. These 16

members are bias-corrected individually for all input variables.

Apart from inherited GCM bias, RCMs simulations are also prone to internal systematic biases due to

physical parameterization schemes. Different parameterization schemes induce dry or wet biases

depending upon activation and closure assumptions. A detailed study to analyze the strength of three

different cumulus parameterization schemes (Betts-Miller-Janjić (BMJ)16,17

, Kain-Fritsch (KF)18

, and

Grell and Dévényi (GD)19

) in simulating monsoon seasonal rainfall and their possibilities to infuse biases

shows that GD underestimates the heavy precipitation (>10mm/day) while KF overestimates the heavy

precipitation as seen in rainfall rate probability density function (PDF)20

. At higher resolution (<10km),

cumulus parametization can be switched off (i.e. grey-zone) as grid-scale microphysics is sufficient to

resolve sub-grid processes. Recent studies have shown that WRF grey-zone resolution (9km) captures the

monsoon intra-seasonal oscillations as well as the spatio-temporal evolution of Indian summer monsoon

rainfall (ISMR)21

. Yue Zheng and co-authors performed WRF experiments in three convection treatments

- with KF, modified KF scheme22

(multi-scale KF or MSKF) and no Cumulus. In MSKF, the adjustment

mean time and entrainments processes are modified and it has grid resolution dependency. It is found that

with MSKF, excess rainfall amount is reduced with improvements in predicting location as well as

intensity of surface precipitation at high resolution of 3km and 9km22

. Downscaled ERP (hereafter

ERPWRF) has been tested with different physics options beforehand and WRF double moment 6-class23

is found to show better results with MSKF18

cumulus scheme (Physics tests are not included in this

report).

5

Some recent studies indicate that changes in land-use land-cover (hereafter LU/LC) pattern can induce

significant changes in temperature and wind patterns and also in the seasonal rainfall24,25

. Deforestation

for urbanization, agriculture and other developmental works can intensify climate change issue, thereby

increasing frequency of weather hazards. Therefore, it is expected that usage of better LU/LC datasets

over Indian region in the RCMs may improve the prediction of extreme events.

This work documents the initial findings of reconstructed strategy based on bias-correction and dynamical

downscaling. In order to test the impact of land-use change on extreme event simulations, we have

incorporated LU/LC data for Indian region obtained from National Remote Sensing Centre (NRSC)26

in

WRF for downscaled simulations. Three different extreme events which includes two cyclones Roanu

(2016) and Mora (2017) and Uttarakhand heavy precipitation (2013) case as described in Table 1, are

selected for the present study.

2. Data and Methodology:

2.1 Models

The IITM/IMD ERP is an ensemble prediction system with 16 ensemble members based on coupled

model CFSv2 and the atmospheric model GFS, both at two resolutions T382 (~37 km) and T126 (~100

km) running in forecast and hindcast mode on weekly basis. The ERP has 4 ensemble members each from

four variants (CFS-T382, CFS-T126, GFS-T382 and GFS-T126). Regional model used in our study is

WRF with Advanced Research WRF dynamic solver (WRF-ARW version 4.0); which is a meso-scale

numerical modeling system used for research and forecasting applications in atmospheric sciences27

. All

the ensembles of ERP are regridded to 1x1 degree resolution, bias-corrected and are given as ICs and

LBCs to WRF. The horizontal resolution for the single domain ERPWRF is 9 km with domain centered at

India having 1001x945 grid points (250S - 55

0N; 30

0E - 128

0E) and 37 vertical levels. Downscaling is

attempted with micro-physics WRF double moment class-6 (WDM6)23

and cumulus parameterization

MSKF18

, Yonsei University scheme is planetary boundary layer (pbl) physics28

, Dudhia scheme29

for

shortwave radiation and RRTM scheme30

for long wave radiation. Detailed schematic of downscaled ERP

is given in Figure1.

2.2 Bias-correction & dynamical downscaling

Output from IITM/IMD ERP models i.e. all 16 members are bias-corrected for model mean with 6-hourly

Climate Forecast System Reanalysis (CFSR)31

data and then individually given to WRF-ARW model as

6

ICs and LBCs. Each and every variable of ERP outputs that goes as LBCs to WRF is bias-corrected with

reference to the reanalysis data.

(1)

is model anomaly which is superimposed over observed mean as follows,

(2)

i.e. (3)

Variables which are bias-corrected and given as inputs to WRF include vertical profiles of temperature,

winds, humidity, geo-potential and surface variables SST, mean sea level pressure (MSLP), soil-moisture,

soil-temp, snow depth etc.

LU/LC data from ISRO-NRSC26

is incorporated in WRF for Indian domain. Observational datasets used

to verify the model predictions are IMD-TRMM merged rainfall32

and ERA5 reanalysis33

for winds and

MSLP.

2.3 Objective tracking algorithm

Cyclone tracks of raw-ERP and ERPWRF are plotted using objective tracking algorithm which is a

modified version of Geophysical Fluid Dynamics Laboratory (GFDL) vortex-tracker developed by

NOAA/NCEP34

. This algorithm is widely used to locate storm position in multi-model ensemble

prediction system34,35

.

3. Results & Discussions:

We carried out different experiments with downscaling forecast setup for different extreme cases, few

final experiments are explained here (see Table 2) for cyclone Roanu with one ensemble member i.e.

control run of CFS-T382. In order to understand the importance of bias-corrections on ERPWRF,

comparison is made between the downscaled forecast runs with bias-correction and without bias-

correction. Also, the impact of land-use in WRF using default dataset i.e. United State Geological Survey

(hereafter USGS) and NRSC-LU/LC is compared. After comparison, the better forecast setup is finalized

for present study. Then the runs are made for all 16 members for all three selected cases using the final

setup that includes 6-hour bias-correction using NRSC-LU/LC data in WRF.

7

3.1 Cyclonic Storm Roanu of 2016

The cyclonic storm Roanu occurred during onset phase of southwest monsoon 2016. This system initiated

as low pressure area over north Sri Lanka and adjoining areas of gulf of Mannar and southwest Bay of

Bengal (BOB) on 16 May, developed into a depression on 17th May and deep depression on 18

th May, as

it moved north northeastwards. Further on 19th May, it intensified into cyclonic storm and moving along

the east Indian coast re-curved northeastwards. Finally on 22nd

May, it weakened into depression over

parts of northeast India and Myanmar36

.

3.1.1 Results from Experiments

In the first experiment (noBC_usgs_MSKF), we have not corrected input ICs and LBCs for biases; and

default USGS data is used in WRF. The second experiment (BC_usgs_MSKF) uses bias-corrected WRF

input, but USGS data is kept same. Another experiment (BC_lulc_MSKF) is done with bias-corrected ICs

and LBCs and also by replacing Indian domain USGS data with the NRSC-LU/LC data. (please refer

Table 2 for experiment details)

Figure 2 depicts observed rainfall along with rainfall from raw-ERP (control CFS-T382) and

noBC_usgs_MSKF rainfall. In observed rainfall, cyclonic system is developed near Sri Lanka and moved

northeastwards along the east coast of India. In raw-ERP, this system is slightly away from Sri Lanka

coast and seems to be stagnant near Tamil Nadu and Andhra coast during 16th to 20

th May. In the case of

noBC_usgs_MSKF, this system has developed at right location and started moving into the eastern

peninsular coast instead of moving towards northeast. Similar features are noted from the vorticity and

MSLP plots in Figure 3.

Rainfall spatial plot for BC_usgs_MSKF, given in right most column of Figure 4, shows a clear

movement of the system similar to the observed but slightly away from the coast. The development and

directional movement of system is found to be improved with bias-correction. Figure 5 for vorticity and

MSLP support this movement but it is seen that a strong false system is predicted in Arabian Sea near

equator, which is absent in observation (extreme left panel of the figure).

By replacing USGS data with NRSC-LU/LC data over Indian region, the development, intensification

and northeastward movement of Roanu is comparable to observation, as seen from Figure 6 and 7. The

spatial and temporal evolution of the cyclone is better captured by BC_lulc_MSKF. And the false system

over Arabian sea, seen in BC_usgs_MSKF is less intense in the BC_lulc_MSKF. This becomes clearer in

Figure 8 where the difference of rainfall for BC_lulc_MSKF and BC_usgs_MSKF (RainfallBC_lulc_MSKF-

8

RainfallBC_usgs_MSKF) is plotted. It is found that cyclonic movement is fast in BC_usgs_MSKF and the

Arabian Sea feeble system is more intensified, as compared to BC_lulc_MSKF.

As evident from above mentioned plots, BC_lulc_MSKF is giving more accurate results and close to

observation.

3.1.2 Results from final setup

Based on the results from experiments discussed in previous section, we have finalized the downscaling

forecast setup for this study which includes 6-hourly bias-correction and incorporated NRSC-LU/LC data

in WRF. The model has been run for all 16 ensembles and the ensemble average of WRF output

(hereafter termed as ERPWRF) is analyzed.

Figure 9 shows the observed, raw-ERP multi-model ensemble mean (MME) and ERPWRF rainfall for

the Roanu cyclone. Observed rainfall amount is more than 170 mm extended almost from 770-100

0E, raw-

ERP did capture cyclone vortex but the magnitude and spatial extent of rainfall are not realistic when

compared to the observations. In ERPWRF forecasted magnitude as well as zonal spread of rainfall is

better than raw-ERP. In Figure 10 vorticity (shaded) and mean sea level pressure (MSLP contours) are

shown from 16th-20

th May 2016 for ERA5 reanalysis, raw-ERP, ERPWRF. The vortex in raw-ERP is

moving very slow into the Odisha coast. On the other hand, ERPWRF is giving better intensity of cyclone

with vortex moving northeastward slightly away from coast. Figure 11 shows the track of Roanu cyclone

using objective tracking algorithm for raw-ERP (blue) and ERPWRF (red) compared against IMD best

track (black). Track for raw-ERP shows slow vortex movement and the direction of this system is quite

different from the observation. ERPWRF track is close to the observed IMD track albeit with a shift

towards east. Cyclone Roanu intensity (Figure12 (a)) and position (Figure12 (b)) errors are plotted, it is

evident from Figure12 that in WRF-MME both errors are reduced, Intensity error shows that Roanu

cyclone intensity is improved remarkably in WRF-MME.

3.2 Uttarakhand Heavy Rainfall 2013

Heavy rainfall during 14th

-17th

June 2013 in Uttarakhand caused landslides and flooding resulting into

excessive loss. Studies have shown that this event was a result of interaction of monsoonal flow with the

trough in mid-latitude westerlies (similar to 2010 Pakistan flood)6.

Extended range forecasts of 16 ensemble members from 7th June initial condition is downscaled

individually after bias-correction. Figure 13 shows the time series of area averaged rainfall (in mm/day)

over Uttarakhand region (780E-80

0E, 29

0N-31

0N) plotted for Ensemble members (dotted grey lines),

9

mean of all 16 members i.e. WRF-MME (solid grey line) and IMD rainfall data (black line) that is used

for comparison. Although observed rainfall show two peaks on 11th and 17

th June, the raw-ERP ensemble

mean (blue line) failed to predict these peaks. It is evident that in ERPWRF, few members are giving

rainfall amount comparable to observations. However rainfall is much less in most of the members,

resulting the mean to be around 20mm while the observed rainfall was >100mm on 17th June. Though the

intensity of the rainfall is not captured by WRF-MME, the probability of getting 100mm rainfall is more

than 10% on 17th June from the ERPWRF indicating that the model is able to capture the intensity of the

event to some extent.

This can also be explained from the Figure 14 where the spatial distribution of rainfall (shaded) and

velocity streamlines (850hpa winds) are shown for 14th -18

th June 2013. Observed, raw-ERP and

ERPWRF are compared; raw-ERP could not predict rainfall over the Uttarakhand region during this

period but ERPWRF has realistically captured the rainfall and circulation pattern. Observed circulation

pattern shows interaction between an offshore trough over west coast and BOB low pressure system

moving northwestwards, which was the prominent meteorological aspect as highlighted in earlier study6.

In Figure 15 200hpa wind vector and it's magnitude(shaded) has been plotted, where a deep upper

tropospheric westerly trough over northern part of country can be seen in observation. This upper level

trough has favored unstable local conditions at lower level6 which resulted in the heavy rainfall over the

region. This trough is absent in raw-ERP, whereas in ERPWRF upper level trough is present though not

as prominent as in observation. ERPWRF also captured the northeasterly flow over Arabian Sea similar to

observation which implies that the model could predict overall circulation pattern. Since the dynamics

behind the event is captured by bias-corrected and downscaled setup to some extent, it can be concluded

that the rainfall peak in ERPWRF is realistic and not caused by any false conditions. ERPWRF is able to

give indication with more than 10% probability of 17th June Uttarakhand rainfall (> 9.5cm) almost 10

days in advance from initial condition of 7th June.

3.3 Severe Cyclonic Storm Mora of 2017

A well Marked low pressure area over central Bay of Bengal became depression on 28th May 2017,

moved east northeastwards and same day intensified into cyclonic storm Mora. On 29th May it moved

north northwestward, kept on moving in this direction and finally weakened into depression over

northeast Indian states37

.

Figure 16 depicts the rainfall from observations, raw-ERP and ERPWRF forecasts from 27th to 31

st May

2016. ERPWRF is able to forecast rainfall spatial extent similar to observation but the intensity is very

less. Although the rainfall associated with the cyclone follows observed movement, the vortex is not

10

predicted by ERPWRF (Figure 17). The raw-ERP has not shown the rainfall intensity and movement of

the cyclone and no vortex formation is seen (Figure 17). Since objective tracking algorithm decides the

track based on threshold values of vorticity and MSLP, which are not fulfilled by either raw-ERP or

ERPWRF, the track could not be plotted for cyclone Mora. Its noticed that there are some shortcoming in

predicting Mora cyclone with respect to its intensity and track compared to observation. Still it is better

captured in ERPWRF compared to raw-ERP.

4. Conclusions and Remarks

An assessment of bias-correction and downscaling forecast strategy with an aim to improve the extreme

events in extended range prediction is presented in this study. For this, the ERP forecasts are downscaled

to 9km resolution after bias-correction. Initial results suggest that bias-corrected and downscaled ERP

outputs shows better evolution of selected extreme events. All the runs are started with initial conditions

7-10 days prior to the event and these runs are carried out with 6-class WRF double moment scheme

along with MSKF cumulus parameterization for better representation of the large-scale and the grid-scale

precipitation. Also, the influence of the land-use land-cover has been tested for the tropical cyclone

Roanu. ERPWRF forecasts using NRSC-LU/LC data shows some improvement in predicting the rainfall

intensity and overall system movement. However, as the tested case is an oceanic system, the results does

not show any prominent difference. The impact of LU/LC data in predicting heavy rainfall events over

Indian region is being explored in detail and will be reported shortly in a separate study.

The final forecast setup for the present study uses 6-hourly bias-correction and NRSC-LU/LC data for all

16 members of the ERP. It is found that the ERPWRF forecast of rainfall intensity and track for Roanu

cyclone is better than the raw-ERP. The interaction between monsoon low pressure systems and mid-

latitude westerly trough associated with Uttarakhand heavy rainfall event is predicted to some extent by

ERPWRF. Although the vortex formation for cyclone Mora is absent in ERPWRF, it showed some

improvement in predicting the spatial pattern of rainfall.

The benefit of using forecast strategy based on dynamical downscaling is to take advantage of skillful

coarse resolution forecasts from established prediction system to get more reliable weather information at

higher resolution and with lead time of at least 10-12 days. Besides having few shortcomings, the bias-

corrected and downscaled approach provides optimism in predicting extreme events. The shortcomings

noticed in the present study will be addressed in future studies.

11

Acknowledgements

IITM is fully funded by Ministry of Earth Science, Govt. of India. Model runs are carried out on

High Performance Computer (HPC) Pratyush installed at IITM, Pune. Authors are grateful to the

Dr. P. Mukhopadhyay and Dr. Swapna P. for their constructive comments.

12

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15

Table 1: Experiment details of downscaled ERP Runs

S. NO.

Event

Duration

ERP IC for

forecast

1.

Uttarakhand Heavy Rainfall

16th

-17th

June 2013

0607

2.

Cyclone Roanu

17th

-22nd

May 2016

0510

3.

Cyclone Mora

28th

-31st May 2017

0524

Table 2: Experimental runs for Cyclone Roanu

Sr. No. Experiment for control (CFS-T382)

1. No Bias Correction + WRF Global USGS data noBC_usgs_MSKF

2. Bias Correction + WRF Global USGS data BC_usgs_MSKF

3. Bias Correction + NRSC LULC data for India BC_lulc_MSKF

16

Figure1: Schematic of Forecast Strategy for Bias-corrected downscaled ERP

IITM/IMD ERP

CFSv2 T126

CFSv2 T382

GFSv2 T126 (BC-SST)

GFSv2 T382 (BC-SST)

BC-Downscaled ERP

WRF-CFS126

WRF-CFS382

WRF-GFS126

WRF-GFS382

WRF

multimodel

ensemble

mean (WRF-

MME)

17

Figure 2: Comparison of Rainfall (mm/day) for the control (CFS-T382) run of Roanu

Cyclone for downscaled without bias corrections with observation and raw from 16th

-20th

May 2016 (top-bottom)

18

Figure 3: same as figure 2, but for vorticity x10-5

s-1

(shaded) and mslp (contours)

19

Figure 4: Comparison of bias corrected downscaled rainfall (mm/day) for control (CFS-

T382) of Roanu Cyclone using old USGS data (WRF) with observation and raw from 16th

-

20th

May 2016 (top-bottom)

20


s-1


21

Figure 6: Evolution of Bias corrected downscaled rainfall for control (CFS-T382) of Roanu

cyclone using NRSC LU/LC data for Indian domain(WRF) from 16th

-20th

May 2016 (top-

bottom)

22


s-1


23

RF(NRSC_LULC) - RF (WRF_USGS)

Figure 8: Difference of Bias corrected downscaled rainfall for control (CFS-T382) run with

NRSC LU/LC data for Indian domain(WRF) and WRF USGS data for Roanu cyclone

24

Figure 9: same as figure 6 but for WRF-MME average(16 ensembles)

25

Figure 10: same as figure 7, but for WRF-MME average(16 ensembles)

26

Figure 11: Cyclone Tracks of downscaled ERP(red), raw ERP(blue) and Observed (black)

Using Objective Tracking Algorithm

27

Figure 12: Cyclone Roanu Track Errors

28

Figure 13:Time series of Multi-model ensemble mean for bias corrected downscaled

IITM/IMD (grey lines), raw-ERP MME(blue line) and observed (black) rainfall (mm/day)

averaged over region 78E-80E, 29N-31N for Uttarakhand Heavy Rainfall Event 2013

29

Figure 14: Spatial evolution of Rainfall mm/day (shaded) and 850hpa wind m/s

(streamlines) for Uttarakhand Event from 14th

-18th

June 2013 (top to bottom)

30

Figure 15: Spatial Evolution of 200hpa wind magnitude m/s (shaded) and vectors for

Uttarakhand Event from 14th

-18th

June 2013 (top-bottom)

31

Figure 16: Ensemble Mean Rainfall (mm/day) of bias corrected downscaled ERP, raw ERP

compared with observations for Mora Cyclone from 27th

-31st May 2017 (top to bottom)

32

Figure 17: same as figure 16 but, for Vorticity x10-5

s-1

(shaded) and Mean Sea level

Pressure (contours)

bias-correction and dynamical downscaling strategy to

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