Estimativa de Precipitação por...

Estimativa de Precipitação por Satélite

Treinamento GeonetCast - 22 de junho de 2016

• Precipitation is a vital component of the hydrological cycle on the Earth.

• Weather radars and rain gauges (primary source of rainfall) are typically restricted to populated areas on the Earth and can only extend out over water bodies 150 km or so.

Satellite-based methodologies serve to fill in these

huge data voids, especially over unpopulated

regions and oceans.

SATELLITE RAINFALL RETRIEVAL

WHY USE SATELLITES?

Significantly expands spatial coverage over

continental areas and over oceans

No beam block (mountains, buildings, insects)

No signal attenuation effects

Less need for calibration- often using only one

instrument (such as GOES)

Supplements ground based radar and rain

gauge estimates

Rainfall Requirements Depend Upon the Application

• Monthly average rainfall, global (±60o latitude), pentad-type (e.g. 2.5-degree boxes), over land/ocean (Climatic shifts? Desertification?) • Daily accumulated rainfall and snowpack, many stations over a watershed (When do I release water from a reservoir? Allocate water distribution?) • “Realtime” global or regional analysis of rainrate at the best possible horizontal resolution (hydrological models) • 5-minute updates of point rainfall inside an area (e.g., 105 km2) during the lifetime of a thunderstorm or landfalling hurricane away from coastal radars (Should coastal or low-lying areas be evacuated? Temporarily relocate naval fleet to safe harbor?) • Any indications that this winter is associated with El Nino conditions? (An energy company, a tree removal company, emergency services)


Satellite-based rainfall estimation methods

• Satellite rainfall retrievals are generally categorized into LEO and GEO. • Retrieval algorithms are typically classified on their observing spectrum (VIS, IR, PMW, AMW) or “multi-spectral” (i.e., use of one or more of these individual spectrums). • If the methodology uses multiple satellites or other information such as radar or gauges is classified as a “blended” technique.


IR & VIS Techniques

The rainfall retrieval in these wavelengths is based on the fact that bright

(optically thick) clouds are positively correlated with regions of convective

rainfall. On the other hand, clouds with cold tops in the IR imagery produce more

rainfall than those with warmer tops.

Visible (VIS) and Infrared (IR) techniques are rather simple to apply

Show a relatively low degree of accuracy.

GEO weather satellite VIS and IR imagers uniquely provide the rapid temporal

update cycle (e.g, 30 minutes or less) needed to capture the growth and decay of precipitating clouds..

Rainfall estimation methods can be divided in the following categories: cloud-indexing, bi-spectral schemes, life history, and cloud model-based.


~ Ts4

~ Tn4

No Infravermelho a radiação não

penetra as nuvens, logo o Satélite esta medindo a emissão do topo da nuvem. A questão é: Como podemos inferir a chuva sem saber o que existe dentro dela? Desse modo podemos dizer que temos uma solução empírica


• O valor médio da taxa de chuva para cada Temperatura de Brilho foi calculado e obteve-se o

gráfico da figura a seguir:

• Uma curva de regressão com valor de Taxa de Chuva sendo a variável dependente e Temperatura de Brilho a variável independente foi estabelecida. O modelo encontrado foi:

Chuva= 16,3585 – 0,7855 * TB + 0,0136 * TB 2 – 0,00007 * TB 3


IR & VIS Techniques

Nimbostratus

Cumulonimbus

Cirrus

Nimbostratus

Cumulonimbus

Cirrus


This is a fully automated method using an empirical power-law function that generates rainfall rates (mm/h) based on GOES-8 channel 4 brightness temperature.

Sample Hydro-Estimator Rain Rate Curves as a Function of Precipitable Water

Hydroestimator


Hydroestimator : Correction factors

Moisture correction factor (PWRH): defined as the product of precipitable water (PW) (integrated over the layer from surface to 500 hPa) times the relative humidity (RH) (mean value between surface and 500 hPa., in percentage) is applied to decrease rainfall rates in dry environments and increases them in the moist ones.

Screening method: This technique assumes that raining pixels are colder than the mean of the surrounding pixels.

Orographic effect: A Digital Elevation Model (DEM) with near-surface winds is used to increase the precipitation on the windward side and decrease on the leeward side (Vicente, G. A., J. C. Davenport and R. Scofield, 2000: The role of orographic and parallax correction on real time high resolution satellite rainfall rate distribution. Int J Remote Sens , vol. 23, no. 2, pp. 221-230


Screening method: This technique assumes that raining pixels are colder than the mean of the surrounding pixels.

TT Tave

=

-

The coldest tops are associated with convective rainfall.



• Tơ < -1.5

Convective precipitation: defined essentially by the empirical power-law function corrected by PWRH.

• Tơ = 0

Stratiform precipitation: whose maximum value cannot exceed 12mmh-1 and must be less than the fifth part of the convective rainfall for a given pixel

• –1.5 < Tơ < 0

• Tơ > 0 pp = 0

-1.5

-1.0

0.0

Entirely

Convective

Entirely

Stratiform

Standarized Temperature



Hydroestimator : Correction factors Assimilation of the orographic enhancement on the 24-hour total rainfall accumulation over the USA north-west coast on 25 November 1998. These corrections are assimilated every 15 minutes into the satellite rainfall estimates and summed in the 24-hour totals. (a) Orographic

enhancement with values

up to 3.0 caused by

northwest winds around 20msÕ 1 at 1800 UTC on 25 November 1998. The precipitation distribution with and without orographic enhancements are shown respectively in (b) and (c). The rainfall

rate totals are given in

inches (in) and millimeters

(mm).


Precipitação Instantânea

Imagens Setorizadas

mm por hora


Precipitação Instantânea Precipitação Diária Precipitação Mensal


Falsas Alarmas Probabilidad de Detección

Bias Correlación Heike skill score


Tb=220 ok

Tb=235 ok

Tb=253 ok

•Tmin: Cold top temperature

•Tmean

•Tmax

•Tmedium

Geometric Feature •Size

•Eccentricity

•Frontal orientation

•Shape Index of cloud;

Texture: •Whole cloud texture variation

•Cloud texture gradients

• Local cloud texture variation

•GLCM and ASM

•Entropy and cold core texture variation;

Coldness

Geometric

Texture

Tb oK 200 225 250 275 300

Tb=235 ok

Tb=253 ok

Tb=220 ok Location Topography

PERSIANN CCS: Precipitation Estimation from Remotely Sensed Information using

Artificial Neural Networks – Cloud Classification System

Hsu et al. 1997 and Sorooshian et al. 2000)


PERSIANN CCS

c1

c2

Tb (K)

R (mm/h)

200 300 0

80

KV220

KV253

KV235

Cloud Classification Patch Feature Extraction Image Segmentation Rainfall Mapping

ck

Cloud-Rainfall Mapping _

6_

6Cold

convective

clouds

Cirrus

Middle layer clouds

Warm cloud rainfallHsu et al. 1997 and Sorooshian et al. 2000)


PERSIANN CCS

Diurnal Cycle


Passive MW (PMW) means naturally emitted radiation from the Earth’s surface and atmosphere that interacts with clouds and precipitation and is measured by a radiometer on-board a satellite.

Most passive microwave radiometers launched to date operate in frequencies

ranging from 6 GHz to 190 GHz.

Below 20 GHz, emission by precipitation-size drops dominates and ice particles above the rain layer are nearly transparent. Above 60 GHz, ice scattering

dominates and the radiometers cannot sense the rain drops below the freezing layer.

Emission-based: are mostly applicable over ocean because water surfaces are relatively homogeneous and provide a cold background due to low emissivity.

Scattering-based: Due to the higher, but more varying emissivity of the land surface, the only reliable means of detecting rainfall over land is by isolating depressed TB’s as a result of scattering by millimeter sized ice particles that exist in most rain clouds.

PMW TECHNIQUES

~ Ts

~ Ts + (1- )Tn

~ (1- )Tn

A superfície da Terra emite radiação em microondas. Esta energia interage com os hidrometeoros: absorção, emissão e espalhamento.

Como esta interação depende da Freq., podemos inferir algumas propriedades físicas da nuvem amostrada, ou seja:

Podemos identificar tipos de hidrometeoros: Água liquida, Gelo, Neve.

PMW TECHNIQUES

PMW TECHNIQUES

Nimbostratus

Cumulonimbus

0 C

0 C

0 C

0 C

Emission – freq’s <40 GHzScattering – freq’s >60 GHz

Special Sensor Microwave/Imager (SSM/I)

Physical Retrievals vs. Empirical Methods

• Goddard Profiling Algorithm (GPROF)

• Based on Cloud Structures that may be seen by PMW sensors and statistical inversion techniques.

• Microwave Integrated Retrieval System (MIRS)

• Based on an assimilation-type scheme (1DVAR) capable of optimally retrieving atmospheric and surface state parameters simultaneously.

• Ferraro algorithm

• Utilize both emission and scattering signatures of rainfall over ocean

• Rainfall detection via scattering 85/89 GHz over land. TB depressions indicate possible rain. Larger the depression, heavier rain


•Goddard Profiling

Algorithm (GPROF)


GPM Constellation Concept GPM Core Observatory

(NASA/JAXA, 2014 )

DPR (Ku & Ka band)

GMI (10-183 GHz)

65o Inclination

407 km altitude

5 km best footprint

0.2 – 110 mm/hr and snow

Suomi NPP

(NASA/NOAA)

MetOp B/C (EUMETSAT)

JPSS-1 (NOAA)

DMSP F17/F18/F19/F20 (DOD)

GCOM-W1

(JAXA)

NOAA 18/19

(NOAA)

Megha-Tropiques

(CNES/ISRO)

Next-Generation Unified Global Precipitation Products Using GPM Core Observatory as Reference Precipitation rates everywhere in the world every three hours 28

TRMM (NASA/JAXA)

29 Page

These are the first publically released images from GPM. The images show precipitation falling inside a March 10 extratropical cyclone over the northwest Pacific Ocean, approximately 1,000 miles east of Japan.

GPM Core: First Light Images Released March 25th 2014

Below: 3D view of precipitation rate inside the extra-tropical cyclone as viewed by the DPR. Vertical resolution is 250 m, horizontal resolution is ~5 km.

www.nasa.gov/gpm Above: Image from GMI showing rain rates across a 885 kilometer swath of an extra-tropical cyclone observed on March 10, 2014. In the northwest part of the storm, the blue areas indicate falling snow.

GPM Microwave Imager (GMI) retrievals

Dual-frequency Precipitation Radar data

President Obama viewing GPM data 4/24

http://www.nasa.gov/gpm

30 Page

GPM Core Observatory: New Scientific Capabilities

2. IMERG DESIGN – Processing

IMERG is a unified U.S. algorithm that takes advantage of

• Kalman Filter CMORPH (lagrangian time interpolation) – NOAA

• PERSIANN with Cloud Classification System (IR) – U.C. Irvine

• TMPA (inter-satellite calibration, gauge combination) – NASA

• all three have received PMM support

• PPS (input data assembly, processing environment) – NASA

The Japanese counterpart is

GSMaP

Institutions are shown for

module origins, but

• package will be an

integrated system

• goal is single code system

appropriate for near-real

and post-real time

• the devil is in the details

GSFC CPC UC Irvine

prototype6

Receive/store even-odd IR

files

1

Import PMW data; grid; calibrate;

combine

2

Compute even-odd IR files (at CPC)

3

Compute IR displacement vectors

4

Build IR-PMW

precip calibration

10

IR Image segmentation

feature extraction

patch classification

precip estimation

9

Apply Kalman

filter

8 Build

Kalman filter

weights

7

Forward/backward

propagation

5

Import mon. gauge; mon. sat.-gauge

combo.; rescale short-interval datasets to monthly

Apply climo. cal. RT

P

ost

-RT

13

12

11 Recalibrate

precip rate

5. VALIDATION – 3-Hourly, 0.25° IMERG, 3B42, MRMS for 15 June 2014

IMERG better than 3B42 for precip occurrence IMERG performs modestly better for precip volume

Note: Original footprint GPROF retrievals below 0.1 mm/hr are thresholded to zero

• how this affects IMERG depends on the resolution of the input sensor and subsequent averaging (here 0.25°)

[Courtesy J. Wang (SSAI; NASA/GSFC 612)]

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