flood%forecas,ng%data,%models,%tools,%and%sources ......• 5-day seasonal max • duration ts...

Flood forecas,ng data, models, tools, and sources of predictability

Tom Hopson, NCAR (among others) Charon BirkeB, Univ. of Maryland Daniel Broman, Univ. of Colorado

Robert Brakenridge, Dartmouth Flood Observatory David Yates, NCAR

Large-scale constraints on Extreme Precipitation What we expect – extreme precipitation •  individual storms increase 6-10% /degC (scales

with available moisture) •  high confidence much greater than mean

precipitation, but varies with time-scale, location, season

South Asian Monsoon Precip increases in: •  average •  variance •  5-day seasonal max •  duration

TS

Technical Summary

107

Figure TS.24 | Future change in monsoon statistics between the present-day (1986–2005) and the future (2080–2099) based on CMIP5 ensemble from RCP2.6 (dark blue; 18 models), RCP4.5 (blue; 24), RCP6.0 (yellow; 14), and RCP8.5 (red; 26) simulations. (a) GLOBAL: Global monsoon area (GMA), global monsoon intensity (GMI), standard deviation of inter-annual variability in seasonal precipitation (Psd), seasonal maximum 5-day precipitation total (R5d) and monsoon season duration (DUR). Regional land monsoon domains determined by 24 multi-model mean precipitation in the present-day. (b)–(h) Future change in regional land monsoon statistics: seasonal average precipitation (Pav), Psd, R5d, and DUR in (b) North America (NAMS), (c) North Africa (NAF), (d) South Asia (SAS), (e) East Asia (EAS), (f) Australia-Maritime continent (AUSMC), (g) South Africa (SAF) and (h) South America (SAMS). Units are % except for DUR (days). Box-and-whisker plots show the 10th, 25th, 50th, 75th and 90th percentiles. All the indices are calculated for the summer season (May to September for the Northern, and November to March for the Southern Hemisphere) over each model’s monsoon domains. {Figures 14.3, 14.4, 14.6, 14.7}

of precipitation even if atmospheric circulation variability remains the same. This applies to ENSO-induced precipitation variability but the possibility of changes in ENSO teleconnections complicates this gener-al conclusion, making it somewhat regional-dependent. {12.4.5, 14.4, 14.8.3–14.8.5, 14.8.7, 14.8.9, 14.8.11–14.8.14}

TS.5.8.4 Cyclones

Projections for the 21st century indicate that it is likely that the global frequency of tropical cyclones will either decrease or remain essentially unchanged, concurrent with a likely increase in both global mean trop-ical cyclone maximum wind speed and rain rates (Figure TS.26). The influence of future climate change on tropical cyclones is likely to vary by region, but there is low confidence in region-specific projections. The frequency of the most intense storms will more likely than not increase in some basins. More extreme precipitation near the centers of tropical cyclones making landfall is projected in North and Central America, East Africa, West, East, South and Southeast Asia as well as in Australia and many Pacific islands (medium confidence). {14.6.1, 14.8.3, 14.8.4, 14.8.7, 14.8.9–14.8.14}

40 N

60 W 0 60 E 120 E 180

20 N

EQ

20 S

40 S

NAMS

SAF

SAS

EASNAF

AUSMCSAMS

Regional land monsoon domain

120 W

90 % tile75 % tile50 % tile25 % tile

10 % tile

6040

0-20-40-60

20

Pav Psd R5d DUR

(e) EAS

6040

0-20-40-60

20

Pav Psd R5d DUR

(f) AUSMC

(a) GLOBAL40

20

0

-20GMA GMI Psd R5d DUR

6040

0-20-40-60

20

Pav Psd R5d DUR

(b) NAMS

Chan

ge (%

or d

ays)

6040

0-20-40-60

20

Pav Psd R5d DUR

(c) NAF6040

0-20-40-60

20

Pav Psd R5d DUR

(d) SAS

6040

0-20-40-60

20

Pav Psd R5d DUR

(h) SAMS6040

0-20-40-60

20

Pav Psd R5d DUR

(g) SAF

Stan

dard

dev

iation

of N

ino3

index

(°C)

1.2

1

0.8

0.6

0.4PI 20C RCP4.5 RCP8.5

Figure TS.25 | Standard deviation in CMIP5 multi-model ensembles of sea surface temperature variability over the eastern equatorial Pacific Ocean (Nino3 region: 5°S to 5°N, 150°W to 90°W), a measure of El Niño amplitude, for the pre-industrial (PI) control and 20th century (20C) simulations, and 21st century projections using RCP4.5 and RCP8.5. Open circles indicate multi-model ensemble means, and the red cross symbol is the observed standard deviation for the 20th century. Box-and-whisker plots show the 16th, 25th, 50th, 75th and 84th percentiles. {Figure 14.14}

%

Yr 2100

AR5

Historical Simulation

River flow

Precipitation Soil moisture

Observed Data

Past Future

SNOW-17 / SAC

Sources of Predictability

1.  Run hydrologic model up to the start of the forecast period to estimate basin initial conditions; the flows created can then be “advected” downstream

Model solutions to the streamflow forecasting problem…


River flow


Historical Data Forecasts

Past Future

SNOW-17 / SAC

1.  Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;

2.  Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts.

Sources of Predictability Model solutions to the streamflow forecasting problem…

?


River flow


Historical Data Forecasts

Past Future

SNOW-17 / SAC

1.  Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;

2.  Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts.

Sources of Predictability Model solutions to the streamflow forecasting problem…

Flood forecas,ng data, models, tools, etc.

Data assimila,on approaches

Rain gages radar

Hydrologic modeling

Hydraulic modeling Remotely-‐sensed

soil moisture Remotely-‐sensed river widths

Remotely-‐sensed river al,metry

Global circula,on model ensemble forecasts

Mesoscale weather forecasts

Physically-‐based hydrologic modeling approaches

Data-‐based hydrologic modeling approaches

Weather sta,on observa,ons

In situ river stage measurements

Snow measurements

Isochrones

Time scale

Spa,al Scale

Time scale

Spa,al Scale

Rain gages

radar In situ river stage

Remotely-‐sensed river al,metry and width

Satellite precipita,on

Time scale

Spa,al Scale

Rain gages




nowcas,ng

Mesoscale weather forecas,ng with data assimila,on

Global circula,on model Ensemble

Time scale

Spa,al Scale

Rain gages




nowcas,ng

Mesoscale weather forecas,ng with data assimila,on

Global circula,on model Ensemble

Hydraulic modeling

Data-‐based hydrologic modeling

Physically-‐based hydrologic modeling

Outline I.  Precipita,on products

•  QPE: Rain gauges telemetric systems, Radar, satellite precipita,on es,mates

•  QPE/QPF “nowcas,ng” •  QPF NWP: mesoscale and ensemble medium-‐range GCM

II. Global forecast systems •  Satellite-‐based systems

•  Hydrologic Research Center •  NASA GFMS

•  NWP Ensemble-‐based systems •  Unified systems

•  USA System, Mid-‐Atlan,c River Forecas,ng Center •  European EFAS, France •  Climate Forecas,ng Applica,ons for Bangladesh

II. New river measurement technologies for flood forecas,ng

• Most gauges are placed near permanent seUlements rather than distributed evenly

Measurement of Precipita,on – Limits of Rain Gauges:

Sends and receives horizontal & vertical polarized radiation

Image courtesy Terry Schuur

Dual Polarimetric Radar

•  Satellite precipitation estimation useful in areas with poor radar & rain gauge coverage•  Although satellite sampling more consistent than radar sampling, generally less accurate, with infrared less accurate than passive microwave sensors

OutlineI.  Precipitation products

•  QPE: Rain gauges telemetric systems, Radar, satellite precipitation estimates

•  QPE/QPF “nowcasting”•  QPF NWP: mesoscale and ensemble medium-range GCM

II. Global forecast systems•  Satellite-based systems

•  Hydrologic Research Center•  NASA GFMS

•  NWP Ensemble-based systems•  Unified systems

•  USA System, Mid-Atlantic River Forecasting Center•  European EFAS, France•  Climate Forecasting Applications for Bangladesh

II. New river measurement technologies for flood forecasting

Nowcas,ng defini,on – descrip,on of the current state of the weather in detail and the predic,on of changes in a few hours

WHAT IS NOWCASTING Originally defined by Browning for the 1st Nowcas,ng Conference in 1981 as:

O-‐6 hr forecas,ng by any method

spa,al scale of no more than a few kilometers (1-‐3 km) with frequent updates (5-‐10 min) Heavy emphasis on observa,ons

Jim Wilson

East

Nor

th

Storm Echo at Time-1

Time-2

Time-3

Time-4Nowcast forTime-5

Storm track

Storm Motion Vector

Extrapolation

Jim Wilson

Increasing Forecast Length

less

more

Nowcast

Schematic Representation of Forecast Skill R

elat

ive

Fore

cast

Ski

ll

Numerical Models

•  Nowcast skill decreases rapidly with leadtime

•  High-resolution NWP required for predicting storm organization.

•  Blending optimally combines Nowcast and NWP

Radar data assimilation

CoSPA Technical Review Panel : May 16, 2011

Blending

Some Blending REFS Golding 1998 Pierce 2001 Lin et al 2005 Bowler 2006 Yeung et al. 2009 Kitzmiller 2010 Atencia et al. 2010 Pinto et al. 2010

James Pinto

Archive Centre

Current Data Provider

NCAR NCEP

CMC

UKMO

ECMWF MeteoFrance

JMA KMA

CMA

BoM CPTEC

IDD/LDM

HTTP

FTP

NCDC

Unique Datasets/Software Created Thorpex-Tigge

Early May 2011, floods in southwestern Africa

Early May 2011, floods in southwestern Africa -‐-‐ examine ens forecasts … ECMWF 5-‐day precip

•  Established in 1993 as a nonprofit research, technology transfer, and training organization. •  HRC was created to help bridge gaps between scientific research in hydrology and applications for the solution of important societal problems that involve water.

www.hrc-‐lab.org

NASA Real-‐,me Global Flood Es,ma,on System (GFMS)

•  quasi-‐global (tropics and mid-‐la,tudes) •  satellite precipita,on from TRMM Mul,-‐satellitePrecipita,on

Analysis [TMPA]) -‐-‐ IR and microwave instruments used •  Univ of Oklahoma hydrologic model •  flood es,mates every three hours •  calculates water depth and streamflow at each grid (at 0.125

la,tude-‐longitude) •  Flood detec,on based on water depth thresholds calculated from a

13-‐year retrospec,ve

Mature ensemble-‐based systems

European Flood Awareness System US Na,onal Weather Service, North Central River Forecast Centre (NCRFC) Climate Forecast Applica,ons in Bangladesh (CFAB) UK Flood Forecast Centre Swedish Meteorological and Hydrological Ins,tute (SMHI) Electricité de France (EDF) Water Management Centre of The Netherlands (WMCN) Meuse forecasts Bonneville Power Authority

River Forecast Centers

Weather Forecast Offices

Radar Sites across the US

Scott Ellis

Above-Critical-Level Cumulative Probability

7 day 8 day

9 day 10 day

3 day 4 day

5 day

7 day 8 day

9 day 10 day

Brahmaputra DischargeForecast Ensembles

2004 Brahmaputra Ensemble Forecasts and Danger Level Probabilities

2007 Brahmaputra Ensemble Forecasts and Danger Level Probabilities

7-10 day Ensemble Forecasts 7-10 day Danger Levels

7 day 8 day

9 day 10 day

7 day 8 day

9 day 10 day

-- Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) & NASA TRMM(Future: Global Precipitation Measurement System) - - Utilizing 36-37Ghz (unaffected by cloud)- - pixel size ~20km- - ~2day complete global coverage (night-time brightness temperatures)- - data range: 1997 to present

Objective Monitoring of River Stage and Flow:Satellite-based Passive Microwave Radiometer

Other Approaches: satellite altimeter-derived water level (and discharge derived through rating curve):e.g. Birkett, 1998; Alsdorf et al. 2000; Jung et al. 2010, Papa et al. 2010, Alsdorf et al. 2011, Biancamaria et al. 2011

MODIS sequence of 2006 Winter Flooding

2/24/2006 C/M: 1.004 3/15/2006 C/M: 1.029 3/22/2006 C/M: 1.095

Satellite Altimetry – Jason 2Traditionally used for sea level

Satellite Altimetry – now used for river heights with potential for downstream flood forecasts

for Bangladesh FFWC

37

738

Figure 6. Ground tracks or virtual stations of JASON-2 (J2) altimeter over the GB basin shown 739

in yellow lines. The locations where the track crosses a river and used for deriving forecasting 740

rating curves is shown with a circle and station number. Circles without a station number 741

represent the broader view of sampling by JASON-2 if all the ground tracks on main stem rivers 742

and neighboring tributaries of Ganges and Brahmaputra are considered. 743

744

NCAR

Summary

1.  U,lity of flood forecast systems dictated by the precipita,on product at their core

2.  Effec,ve flash flood guidance (FFG) dominated by skillful es,mates of local rainfall processes with spa,al precision

3.  FFG tradi,onally based on telemetric rain (and stream) gage networks

NCAR

Summary (cont)

1.  More recently, FFG u,lizes dual-‐polar radar with greater spa,al sampling and “nowcas,ng” capabili,es – but requiring more “overhead” to maintain

2.  River flood forecas,ng (RFF) (medium to large catchments) requires less “local” and more “regional” knowledge of rainfall-‐runoff processes, and upstream catchment condi,ons

NCAR

Summary (cont)

1.  RFF also benefit from lower requirements in rainfall spa,al precision, and can thus u,lize numerical weather predic,on (NWP)

2.  Larger catchments can benefit from long-‐lead weather forecasts (5-‐15 days), but which are inherently probabilis,c (ensembles)

3.  Ensemble RFF must account for uncertain,es introduced throughout the “forecas,ng chain” to truly be effec,ve in user decision making

NCAR

Summary (cont)

1.  Indirect satellite measurements of river discharge (changes in river width or height) provide new poten,al for flood warnings by travel ,me lags in upstream water flow

“I have a very strong feeling that science exists to serve human welfare. It’s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.” Dr. Walter Orr Roberts (NCAR founder)

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