Flash Flood & CREST
(NOAA-Cooperative Remote Sensing Science and Technology Center)
(Hydro-Climate & Land Hydrology)
Reza Khanbilvardi (CREST Director)
NOAA Eastern Region Flash Flood Conference, Wilkes-Barre/PA,
June 3, 2010
Flash Flood Forecasting
INPUT Information
Observation or
Estimation of
Hydrologic Variable:
Precipitation
(Rainfall & Snowfall)
from: Rain Gauge,
Radar, Satellite
Calibration Data
In Situ Data
Watershed & Stream
Characterization:
Hydrograph
Topography, roughness
Stream Flow
Snowmelt
Soil Moisture
Vegetation density,
Reservoir Release
Infiltration, Interception
Hydrologic Models
Products
Surface
Runoff
Operation
CREST Related Actvities/Projects:
To Improve Flash Flood Forecasting:
Precipitation Enhancement:
• Remotely Sensed Precipitation Estimation & Improvement
• Precipitation Prediction using Satellite-based Information
Soil Moisture Enhancement
•
•
•
•
CREST L-band and High Frequency Microwave Radiometer
using Passive & Active Microwave for Soil Moisture Estimation
Soil Moisture & Hydrological Modeling
Application of SMAP for Flash Flood Forecasting Test-bed
Snow Characteristics & Flash Flood Guidance (FFG) Syatem
Sea Ice Monitoring
Improve Coastal and Estuarine Flood Monitoring
Development of a sub-watershed hydrologic model in Puerto Rico
global retrieval of microwave land surface emissivity Improvement
Precipitation Estimation & Nowcast
Improvement
• MW based Snowfall Detection & Estimation,
• Multi Sources Rainfall Estimation to Generate Rainfall for Radar Gap
Areas,
• Validation and Improvement of Satellite based Rainfall Products
• Satellite-based Thunderstorm Nowcasting,
MW-based Snowfall Detection & Estimation
Data Used: AMSU-B channels:
89-, 150-, 183±1-, 183±3-, 183±7 – GHz & Ground-based snowfall
Methodology: An Artificial Neural Networks System (ANN)
• Data Selection
Model Performance
Step # 2
Station data
25% of data for Testing
no
Surface type
= 1 (land)
- Data Filtering
yes
• Model Features:
no
Precipitation
type?
discard
data
yes
no
no
discard
data
Tsurf<273°K
- Channel Combinations,
- Number of Nodes
- Transfer Functions
- Number of Runs
discard
non-precipitating
data
pixels
Only snow?
discard
data
yes
yes
Step # 2
no
Precipitation
> 0.0 inches
discard
data
Snowfall Estimates (mm)
AMSU
data
- 75% of data as Input &
Snowfall Estimates (mm)
Data Filtering
150-, 183±3- and 183±7 GHz
R=0.869
Calibration
4
3.5
RMSE=0.22
3
2.5
2
1.5
1
0.5
Snowy pixels
Model Estimate
(Snowfall Rate)
0
0
To model
0.5
1
1.5
Model
2
2.5
3
3.5
4
Snowfall Obs. (mm)
1.00
Cali
1.0
Vali
150, 183 ± 1, 183 ± 7
Calibration
150, 183 ± 1, 183 ± 7
0.90
Validation
0.9
0.80
0.70
0.8
R corrected
0.60
0.7
0.50
0.30
0.5
0.20
0.10
0.4
0.00
0
0.3
10
15
20
25
30
# of RUNS with P-value < 0.5
0.1
1.0
Calibration
Validation
25 cal
25 val
0.8
35
2.5
R=0.600
RMSE=0.34
Validation
2
1.5
1
0.5
R (average)
0.7
0
0
0.6
1
1.5
2
2.5
Snowfall Observation (mm)
0.4
0.3
0.2
0.1
0.0
0.5
Snowfall Obs. (mm)
0.5
10
14
15
20
23
27
28
30
31
38
40
42
48
50
54
68
71
72
73
74
98
103
104
108
112
113
114
117
121
173
174
175
176
177
183
187
188
191
193
216
89, 150, 183 ± 1, 183 ± 3 and 183 ± 7 GHz
89, 150, 183 ± 3 and 183 ± 7 GHz
89, 183 ± 1, 183 ± 3 and 183 ± 7 GHz
150, 183 ± 1, 183 ± 3 and 183 ± 7 GHz
150, 183 ± 3, and 183 ± 7 GHz
183 ± 1, 183 ± 3 and 183 ± 7 GHz
89, 150, 183 ± 1, and 183 ± 7 GHz
89, 150, 183 ± 1, and 183 ± 3 GHz
89, 183 ± 3, and 183 ± 7 GHz
150, 183 ± 1, and 183 ± 7 GHz
150, 183 ± 1, and 183 ± 3 GHz
89, 150, and 183 ± 7 GHz
89, 183 ± 1, and 183 ± 7 GHz
89, 183 ± 1, and 183 ± 3 GHz
183 ± 3 and 183 ± 7 GHz
Channel Combination
89, 150, and 183 ± 3 GHz
89, 150, and 183 ± 1 GHz
150 and 183 ± 7 GHz
183 ± 1 and 183 ± 7 GHz
183 ± 1 and 183 ± 3 GHz
89 and 183 ± 7 GHz
150 and 183 ± 3 GHz
89 and 183 ± 3 GHz
150 and 183 ± 1 GHz
183 ± 7 GHz
89 and 150 GHz
89 and 183 ± 1 GHz
150 GHz
183 ± 3 GHz
0.9
183 ± 1 GHz
0.0
5
Nodes in HL
0.2
89 GHz
R (average)
0.40
0.6
Snowfall Estimates (mm)
150, 183 ± 1, and 183 ± 7 GHz
BR - 25-25 - LZA - 13
Snowfall Estimates (mm)
3
# of Nodes in Hidden Layer
Channel Combination
3
Multi-Sensors Precipitation Estimation
Objective: To Develop a Multi-Sensor Rainfall Retrieval Algorithm to Generate more accurate
Rainfall for Radar Gap Areas. In this project, NESDIS Hydro-Estimator & NEXRAD Stage-IV at Hourly
4km x 4km
Procedures (3 steps):
1) Spatial Error Correction: Apply Method of Least Squares (Brogan 1985): the method
of Hills Climb to cluster Rainy pixels because the corresponding clusters are to pick up.
2) Bias Correction
Bias ratio field using Inverse Distance
method provides a more radar like output
both spatially and intensity wise.
3) Merge Rain gauge-Radar-Satellite
Rainfall to Generate Rainfall for Radar
Gap areas
Pixel by pixel based Successive Correction Method
(SCM) has been tried for a selected gap area in the
radar rainfall.
Validation & Improvement of Rainfall Products
 Validation of satellite-based rainfall retrieval algorithms for hurricane storms
Satellite–based Hydro-estimator, GMSRA from NESDIS, PERSIANN and TRMM-3B42 RT rainfall retrieval
algorithms have been evaluated at hourly and daily basis for Five very strong hurricanes: Charley, Frances,
and Jeanne from 2004 and Wilma and Rita from 2005 against NEXRAD Stage-IV.
Hurricane Frances (5 September 2004, 0-23 UTC)
Hydro-Estimator
GMSRA
PERSIANN
TRMM 3B42
 Validation & Improvement of NESDIS Hydro-Estimator
A new algorithm is been developed to detect rainy cloud pixels using visible
and infrared GOSE data. This algorithm improves the performance of the
Hydro-Estimator.
The plots show the preliminary comparison between the Hydro-Estimator
and new algorithm.
Minimizing the index number implies minimizing the average false alarm rate
(FAR), maximizing the average of probability of detection (POD), and
maximizing the average hit rate (HR)
Stage IV Radar
Satellite Thunderstorm Nowcasting
(Transitioning GOES-based Nowcasting Capability into the GOES-R Era)
Joint Project: CREST-CUNY, NWS-MDL, NESDIS, OAR-NSSL, & CIMMS
RDT (Rapid Developing Thunderstorms) Model, developed by Météo-France in the framework of EUMETSAT-SAF
Nowcating.
- Single IR channel statistics used throughout cell lifecycle to evaluate convective activity,
• Cells are detected by whether they form towers higher than
a given BT threshold (6 degrees). Cells are tracked, and all
properties, such as: contours, areas, growth rates, BT min,
BT avg, and BT gradients around the periphery are stored.
• Lookup tables of cell lifecycles are used to determine if the
cell may be convective. Growth rates and roundness of the
top are important parameters.
Steps in Thunderstorm Nowcasting
Cloud Tracking
t1
=>
t2
Storm Detection
t3
=>
Extrapolation
t4
Borrow ideas from existing algorithms that do each step best.
Applying Extrapolation
Extrapolation is based
on RDT cloud lifecycles
study.
Red: previous
Yellow: current
Green: extrapolated
Investigation is needed
to stabilize extrapolation
The RDT model in New York
Data from direct broadcast, 15 min refresh rate
http://air.ccny.cuny.edu/
Land Group: Soil - Snow - Vegetation
Projects:
• Snow cover and snow water equivalent (SWE) retrieval from
active and passive microwave sensors.
• Development of merging algorithms that combines microwave
and thermal infrared observations for soil moisture observations.
• Improve soil moisture and snow retrieval algorithm reducing
vegetation effect using NOAA-CREST Microwave Radiometers.
• To improve flash flood forecasting system using satellite based
gridded soil moisture data (SMAP Testbbed soil moisture data).
• Active Microwave
• Passive Microwave
• Optical Sensors
• Sea ice monitoring using geostationary satellite data
• Integration of the produced soil moisture and snow cover
characteristics maps into hydrological models.
Vegetation
Soil Moisture
Snow Cover
Soil Moisture Estimation & Improvement
• CREST L-Band & High Frequency Microwave Radiometer
• Soil Moisture Estimation using Passive and Active Microwave & Optical
Sensors,
• Soil Moisture Retrieval & Hydrological Modeling,
• Application of SMAP Test-bed for Flash Flood Forecasting
NOAA-CREST L-band Microwave Radiometer
L-Band Radiometer

Frequency: 1.40 to 1.55 GHz (SMAP mission Frequency)

Dual polarization (H, V)
Antenna system: 1.5 x 0.7 meters
Manufacturer: Radiometrics Corporation, Boulder CO.


Research Objectives:
• L-band soil moisture remote sensing field experiments for
calibration and validation of soil moisture retrieval
algorithms.
• Temporal analysis of brightness temperature variation with
respect to measured soil moisture and vegetation
characteristics (NDVI and NDWI) to develop (or strengthen)
vegetation component of Radiative Transfer Model.
• Study the land emissivity variation under a controlled
environment (roughness and vegetation).
• Investigate the impact of inter rainfall time interval on the
retrieval of soil moisture particularly over vegetated areas.
Soil Moisture Research





Sensitivity Analysis of b-factor in Microwave Emission Model
for Soil Moisture Retrieval
Calibration and validation of radiative transfer model for soil
moisture retrieval at low frequency (1.4 GHz) using better
vegetation component.
Neural network and fuzzy logic modeling for soil moisture
retrieval.
Evaluate the impact of land cover heterogeneity on soil moisture
retrieval.
Evaluate the vegetation impact on soil moisture retrieval for
different land cover type.
Impact of land cover heterogeneity
on soil moisture retrieval
Lakhankar et al (2009 b)
Neural network and fuzzy logic modeling for soil moisture retrieval
3800’ N
Soil Moisture Data
165 km x 495 km
(Res. 800 m)
A
3700’ N
Study Area (A and B)
A: 26.4 km x 96 km
B: 31.2 km x 103.2 km
3600’ N
B
SAR Image
350 km x 300 km
(Res. 25 m)
3500’ N
9900’W
9800’W
9700’W
9600’W
9500’W
Lakhankar et al (2009 a)
9400’W
Truth SM
Simulated SM
Seo et al (2010)
Soil moisture retrieval and hydrological modeling
Analysis of an Adaptive NRCS Curve Number
LULC influences CN and is closely related to its changeable
behavior. SM affects the CN values and also contribute to its
variation due to the amount of water infiltrated. LULC and SM,
therefore, are key factors for understanding CN ‘s behavior.
Intraseasonal variation of the CN over a selected watershed in NJ. MOPEX
data has been used. discharge and precipitation observations since 1927
Arizona(SMEX04)
Alabama(SMEX03)
0.25
Georgia(SMEX03)
0.5
SM = 0.318*SWI+0.005
R = 0.753, RMSE = 0.06
0.2
0.5
SM = 0.572*SWI+0.014
R = 0.453, RMSE = 0.10
0.4
0.15
0.3
0.3
0.1
0.2
0.2
0.05
0
0.1
0
0.2
0.4
0.6
0.8
1
ASMR-E 6.9 GHz Soil Wetness Index (SWI)
0
0.1
0
0.2
0.4
0.6
0.8
1
ASMR-E 6.9 GHz Soil Wetness Index (SWI)
North Oklahoma(SMEX03)
0
0
0.2
0.25
0.2
0.2
0.15
0.15
0.1
0.1
0.05
0.05
0.8
1
0.5
SM = 0.599*SWI+0.005
R = 0.161, RMS = 0.06
0.3
0.25
0.6
Iowa(SMEX02)
0.35
SM = 0.486*SWI
R = 0.462, RMS = 0.07
0.4
ASMR-E 6.9 GHz Soil Wetness Index (SWI)
South Oklahoma(SMEX03)
0.35
0.3
SM = 0.486*SWI
R = 0.728, RMSE = 0.12
0.4
SM = 0.53*SWI
R = 0.565, RMS = 0.09
0.4
0
0
0.2
0.4
0.6
0.8
1
0
Arizona(SMEX04)
Alabama(SMEX03)
0.25
0.2
0
0.2
0.4
0.6
0.8
1
ASMR-E 6.9 GHz Soil Wetness Index (SWI)
0
0
0.2
0.4
0.6
0.8
1
ASMR-E 6.9 GHz Soil Wetness Index (SWI)
0.5
SM = 0.572*SWI+0.014
R = 0.561, RMS = 0.09
0.4
0.3
0.3
0.1
0.2
0.2
0.05
0.1
0
0
0.2
0.4
0.6
0.8
1
0
0.1
0
0.2
0.4
0.6
0.8
1
ASMR-E 10.7 GHz Soil Wetness Index (SWI)
North Oklahoma(SMEX03)
0
0
0.2
SM = 0.321*SWI+0.006
R = 0.488, RMS = 0.07
0.6
0.8
1
Iowa(SMEX02)
0.35
0.5
SM = 0.599*SWI+0.005
R = 0.417, RMS = 0.05
0.3
0.25
0.4
ASMR-E 10.7 GHz Soil Wetness Index (SWI)
South Oklahoma(SMEX03)
0.35
0.3
SM = 0.486*SWI
R = 0.761, RMS = 0.12
0.4
0.15
SM = 0.53*SWI
R = 0.546, RMS = 0.14
0.4
0.25
0.2
0.2
0.15
0.15
0.1
0.1
0.3
0.2
Regression analysis between soil wetness indexes [TB
(H) 6.9 GHz] using the end members derived at local and
In-situ soil moisture observed
Regression analysis between soil wetness indexes [TB (H) 10.7 GHz]
using the end members derived at local and In-situ soil moisture
observed
0.1
0.05
0
0.05
0
0.2
0.4
0.6
0.8
1
ASMR-E 10.7 GHz Soil Wetness Index (SWI)
Soil moisture experiment (SMEX) campaigns
Georgia(SMEX03)
0.5
SM = 0.318*SWI+0.005
R = 0.821, RMS = 0.05
0.2
ASMR-E 10.7 GHz Soil Wetness Index (SWI)
0.3
0.1
ASMR-E 6.9 GHz Soil Wetness Index (SWI)
Volumetric Soil Moisture (VSM),(m3/m3) Volumetric Soil Moisture (VSM),(m3/m3)
Volumetric Soil Moisture (VSM),(m3/m3) Volumetric Soil Moisture (VSM),(m3/m3)
Qualitative vs quantitative estimate of soil moisture
0
0
0.2
0.4
0.6
0.8
1
ASMR-E 10.7 GHz Soil Wetness Index (SWI)
0
0
0.2
0.4
0.6
0.8
1
ASMR-E 10.7 GHz Soil Wetness Index (SWI)
SMAP Test-bed data for Flash Flood Forecasting
Snow Model (snow17)
1.
Sacramento based
Soil moisture
2.
Continuous API
based Soil moisture
3.
SMAP testbed
Soil moisture
Precipitation
Snow melt
Evapo-transpiration
Soil moisture
accounting model
Runoff
HL-RDHM
Flow
Flash Flood Forecasting
Comparison and
Evaluation
•
•
To improve flash flood forecasting system using satellite
based gridded soil moisture data (SMAP Testbbed soil
moisture data).
Hydrology Laboratory-Research Distributed Hydrologic Model
(HL-RDHM) developed by NOAA-NWS is currently used for
Flash Flood Forecasting.
River Flow or Stage,
Gauge data
Flash Flood Forecasting Enhancement
• Snow Characteristics Flash Flood Guidance (FFG) System,
• Towards a better global retrieval of microwave land surface emissivity ,
• Sea ice monitoring over the Caspian Sea using geostationary satellite data
• Improve Coastal and Estuarine Flood Monitoring
• Development of a sub-watershed hydrologic model within the western PR
study basin
Snow Characteristics /Flash Flood
Guidance (FFG) System
CREST High Frequency Microwave Radiometers
C-Band Radiometer



Frequency: 37 and 89 GHz
Dual polarization (H, V)
Manufacturer: Radiometrics Corporation, Boulder CO.
Research Objectives:
• Use high frequency microwave radiometer field experiments for
calibration and validation of Snow cover and SWE retrieval algorithms.
• Temporal analysis of brightness temperature variation with respect to
snow depth, SWE, and Snow Grain Size.
Towards a better global retrieval of microwave
land surface emissivity
Model
Atmospheric correction according to Liebe’s model
a)
b)
Where
Products
Upwelling (a) and downwelling (b) brightness temperature as an atmospheric contribution to the satellite
observation on July 14th 2003 at 37 GHz
a)
b)
Comparing 19 GHz in a) and 37 GHz in
b) V and H polarization emissivities
Sea ice monitoring over the Caspian Sea
using geostationary satellite data
The average percentage of
cloud reduction because of
the daily compositing ranged
from 22% to 25%. Daily maps
of ice distribution and
concentration with minimal
cloud coverage were
produced.
MSG SEVIRI full disk false color composited image
and the portion of the image over Caspian Sea
reprojected to latitude-longitude grid on 23 January
2007 at 10:15 AM UTC.
SEVIRI-based sea ice map over the northern part of the Caspian Sea on 28 February
2007 at 11h15 AM UTC (right) and the MODIS true-colour image for the same day (left)
The obtained correlation coefficients with IMS charts for 2007 and 2008
were 0.92 and 0.83 respectively. The technique has been proposed as
one of candidate ice mapping techniques for the future GOES-R ABI
instrument.
TEMIMI, M., ROMANOV, P., GHEDIRA, H., KHANBILVARDI, R. & SMITH, K. (2009) Sea ice monitoring over the
Caspian Sea using geostationary satellite data. International Journal of Remote Sensing, Accepted.
Instantaneous ice maps (left column) and original MSG SEVIRI images on
23 January 2007. False color images in the right column are constructed
with Ch.3 reflectance (red), HRV reflectance (green) and inverted infrared
brightness temperature (blue)
Improve Coastal and Estuarine Flood Monitoring
(Establishing the Application of High Resolution Satellite Imagery)
Example of inland
flooded area in red
Hurricane Charley 2004 track and hurricane eye location on the 08/14/2004
Use of Radarsat 1 images
8/15/2004
(right after hurricane
Charley 2004)
11/14/2005
(low tide conditions)
Additional
flooded area (in
red) can be seen
inland and along
the coast
Development of a sub-watershed hydrologic
model within the western PR study basin
Specific topics being investigated:
•A Flood Forecast Alarm System for Western
Puerto Rico
•Evaluation of Upscaling Parameters and their
Influence on Hydrologic Predictability in Upland
Tropical Areas
•Calibration and Validation of High Resolution
Radar Rainfall Estimation
Remote Sensing of Evapotranspiration in the Caribbean Region
(UPRM E. Harmsen)
A GOES product has been developed for PR and Hispañola to
estimate ground-based solar radiation and evapotranspiration.
An algorithm is being developed to perform a pixel-by-pixel daily
water balance. The algorithm will provide soil moisture content
which is an initial condition for the flood Nowcast model.
Hydro-Climate
Example of Daily Reference
Evapotranspiration (mm) in
Puerto Rico, May 27, 2009
*
Remotely Sensed
Water Balance
Hydro-Estimator
Rainfall
Flood Nowcaster
Testbed Subwatershed
Basin-Scale Model
Upscaling
Procedure
Example of Daily
Reference
Evapotranspiration
(mm) in Haiti and
the Dominican
Republic, March 10,
2010
CREST Hydro-Climate Participants:
NOAA-CREST Scientists:
NOAA Collaborators:
Reza Khanbilvardi
Shayesteh E. Mahani
Arnold Gruber
Brian Vant Hull
Eric Harmsen
Nazario D. Ramirez
Ramon Vasquez
CCNY/CUNY
CCNY/CUNY
CCNY?CUNY
CCNY/CUNY
UPRM
UPRM
UPRM
Ralph Ferraro
Bob Kuligowski
Pedro Restrepo
Mamoudou Ba
Robert Rabin
Cezar Kongoli
Stephan Smith
David Kitzmiller
Daniel Lindsey
John R. Mecikalski
NESDIS
NESDIS
NWS
MDL
OAR
NESDIS
MDL
NWS
CIMMS
CIMMS
CREST Land Participants:
NOAA-CREST Scientists:
NOAA Collaborators:
Reza Khanbilvardi
Marouane Temimi
Alvaro Gonzales
Pradipat Sumakal
Naira Chaouch
Lenny Roytman
Atiq Rahman
Tarendra Lakhankar
Amir Azar
CCNY- CUNY
CCNY- CUNY
CCNY-CUNY
CCNY-CUNY
CCNY-CUNY
CCNY-CUNY
CCNY-CUNY
CCNY-CUNY
CCNY-CUNY
Peter Romanov
Fuzhong Weng
Sid Boukabara
Jerry Zhan
Felix Kogan
Mitch Goldberg
NESDIS
NESDIS
NESDIS
STAR
STAR
NESDIS
Question?