The Impacts of GPS Radio Occultation Data on the Analysis and Prediction

of Tropical Cyclones

Bill Kuo, Xingqin Fang, and Hui Liu

UCAR COSMIC

α

GPS Radio Occultation

GPS RO observations advantages for tropical cyclone prediction

• There are considerable uncertainties in global analyses over data void regions (e.g., where there are few or no radiosondes), despite the fact that most global analyses now make use of satellite observations.

• GPS RO missions (such as COSMIC) can be designed to have globally uniform distribution (not limited by oceans, or high topography).

• The accuracy of GPS RO is compatible or better than radiosonde, and can be used to calibrate other observing systems.

• GPS RO observations are of high vertical resolution and high accuracy.

• GPS RO is an active sensor, and provides information that other satellite observing systems could not provide

• GSP RO provide valuable information on the 3D distribution of moisture over the tropics, which is important for typhoon prediction.

4-Day Ernesto Forecasts with WRF-ARW

Forecast with GPS Forecast without GPS The Actual Storm

Liu et al. (2012, MWR)

WRF/DART ensemble assimilation of COSMIC GPSRO soundings

• WRF/DART ensemble Kalman filter data assimilation system

• 36-km, 32-members, 5-day assimilation

• Assimilation of 178 COSMIC GPSRO soundings (with nonlocal obs operator, Sokolovskiey et al) plus satellite cloud-drift winds

• Independent verification by ~100 dropsondes.

178 COSMIC GPSRO soundings during 21-26 August 2006

From Liu et al. (2012)

Verification of WRF/DART analysis by about 100 dropsondes during the Ernesto genesis stage.

48h-forecast of SLP (starting at 00UTC 25 August)

August 2006 OBS: Observed storm CTRL: No GPS RO assimilation RO: Assimilation all RO data RO6km: No RO data below 6km RO2km: No RO data below 2km

Typhoons affecting Taiwan from 1958 to 2010

Typhoons affecting Taiwan from 1958 to 2010

Topographical influence of the Central Mountain Range on Typhoons

• Central Mountain Range: – Occupies 2/3 of the island of

Taiwan; – More than 200 peaks with

elevation exceeding 3000 m.

• Central Mountain Range: – Influences typhoon tracks – Enhances and modulates typhoon

rainfall

• Interaction of typhoons with Central Mountain Ranges causes: – Heavy rainfall, severe flooding,

and debris flows – Loss of human lives – Significant damage to agriculture,

industry, and properties

From August 6 to 10, 2009, extraordinary rainfall was brought over Taiwan by Typhoon Morakot, breaking 50 year’s precipitation record, causing a loss of more than 700 people and estimated property damage exceeding US$5.5 billion

Observed Rainfall of Typhoon Morakot (2009)

Typhoon Morakot (2009) Max. 24-h gauge 1504 mm Max. 96-h gauge 2874mm

at Chiayi County (windward slope of CMR)

Accumulated rainfall: (a) 96-h on August 6-10 (b) 24-h on August 8-9

* Objective analysis ~450 automatic stations

24-h rain world record 1825 mm

(a) (b)

The spatial distribution of the simulated 96-h accumulated rainfall on August 6-10 over Taiwan

(unit: mm) by 32-member, 4-km ensembles (a) with and (b) without CMR.

OBS

(a) With Taiwan topography • Intensive rainfall areas (>819.2 mm) well captured • Extremes (>2500 mm) captured, with displacements • Peak 3276 mm

(b) Without Taiwan topography • Homogeneous rainfall distribution • No obvious local rainfall enhancement • Peak 615 mm, less than 25%

The Impact of Central Mountain Range on Typhoon Rainfall Distribution

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Challenge of Typhoon Rainfall Prediction

• The quantitative precipitation forecast (QPF) of the topography-enhanced typhoon heavy rainfall over Taiwan is challenging.

• Ensemble forecast is necessary due to various uncertainties.

• Low-resolution ensemble (LREN): computationally cheap, smooth large scales, but systematic under-predicting rainfall.

• High-resolution ensemble (HREN): computationally expensive, more small scales, generally reasonable rainfall amount, but can produce topographically-locked rainfall biases.

• Ensemble tends to have too large track spread after landfall.

Challenge:

How to improve QPF using ensemble at affordable cost?

Ensemble mean? Probability matching?

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Dual resolution ensembles with probability

matching – a new approach Suppose we have two real ensembles:

LREN---Large-sample-size low-resolution ensemble, i.e., 32-member 36-km

HREN---Small-sample-size high-resolution ensemble, i.e., 8-member 4-km

Basic hypotheses:

• LREN can produce reasonable ensemble mean storm track.

• Good relationship between storm position and rainfall.

Basic idea:

Based on LREN mean track, blend rainfall realizations in different resolutions (ignoring timing) to reconstruct a new “synthetic” rainfall ensemble NEWEN:

• Resample size, i.e., 16-member

• On an arbitrary high-resolution grid, i.e., 2-km, by interpolation

20 November 2012

For member: 13

For time 18/8

Two loops of resamplings around LREN mean track

21 November 2012

3-h rainfall RPS

3-h rainfall PM mean

3-h rainfall OBS

Time 18/8-21/8

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Experiments design

• Model: regional WRF-ARW, 36 km, 64 levels, 20 hPa

• Assimilation system: ensemble-based WRF/DART

• Ｃold IC: ECMWF analysis at 2009080500

• Three experiments:

Cold start from EC: No data assimilation

Hot start with GPS: Assimilate GTS and GPS

Hot start no GPS: Assimilate GTS only

• 32-member, 4-day forecast: 2009080600-1000

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cold start WRF/DART cycling analysis

4-day ensemble forecasts

Observations N, GTS (radiosonde, satwnd, buoy, ship, synop, airep, pilot, profiler, qkswnd, amdar,metar)

…00/10

00/5

12/5

00/6

12/6

00/7

IC BC BC BC BC BC BC BC BC

EC cold start

3 sets of ensemble: GPS, NOGPS, EC

4-day ensemble forecasts (no regional DA)

Hot start Hot start

• 24-h assimilation: from 2009080500 – 2009080600 • 96-h forecasts: from 2009080600 – 2009081000 • Stochastic Kinetic Energy Backscattering Scheme is

used for ensemble prediction. • Assimilation of GPS RO data improved track

forecasts for 36-km ensemble

No GPS GPS

NO COSMIC

• Poor ensemble track • Wrong rainfall timing

COSMIC

• Better ensemble track • Correct rainfall timing

OBS

Day 2 (00/7-00/8) Low-resolution

36-km Ensemble mean

High-resolution 4-km

Ensemble mean

Dual-resolution PM tech.

Ensemble mean

Day 3 (00/8-00/9) Low-resolution

36-km Ensemble mean

High-resolution 4-km

Ensemble mean

Dual-resolution PM tech.

Ensemble mean OBS

NO COSMIC

• Poor ensemble track • Wrong rainfall timing

COSMIC

• Better ensemble track • Correct rainfall timing

Impact of GPS RO on T-PAC Typhoon Prediction

SINLAKU

JANGMI

HAGUPIT

ALL 3 TCs

48-h track forecast errors, averaged over 52 runs for three typhoons (Sinlaku, Hagupit, Jangmi): GPS: 104.1 km NOGPS: 137.7 km 24% improvement

COSMIC and COSMIC-2 FORMOSAT-3 Occultations – 3 Hrs Coverage

FORMOSAT-7 Occultations – 3 Hrs Coverage

FORMOSAT-3

FORMOSAT-7

COSMIC-2 and Beyond

• Higher antenna gain will improve

inversions in lower troposphere and PBL

• Tracking GPS, GALILEO, and GLONASS GNSS signals

• Many more soundings, > 10,000/day

• Improved data assimilation methods

• Monitor rapidly changing pre-tornado environment (poor man’s GOES sounder)

• Greater impact on NWP forecasts

• Will significantly improve hurricane track forecasts and improve genesis and intensity forecasts

• Improve impact of infrared and microwave sounders

• Continue climate benchmark observations without gap

• Significant improvement in space weather observing and prediction

COSMIC Occultations–3 Hrs Coverage

COSMIC-2 Occultations – 3 Hrs Coverage

COSMIC-2 (24 deg) TEC Tracks – 24 Hrs Coverage

Summary

• Assimilation of GPS RO data improved analysis of moisture, geopotential height, and wind fields, leading to improved forecasts of tropical cyclone track and intensity, and its associated heavy precipitation.

• GPS RO data in the lower troposphere is crucial for creating a favorable environment for hurricane genesis. If we miss the bottom 6km, we will fail to capture hurricane genesis.

• With advanced Tri-G receiver, COSMIC-2/FORMOSAT-7 will provide a large number of RO soundings with improved accuracy and quality over the tropical lower troposphere, and will have the potential to significantly improve operational tropical cyclone prediction.

• We will need to devote more attention to improve the assimilation of GNSS RO data in the lower tropical troposphere to maximize the impact of GPS RO on tropical cyclone prediction.