Global precipitation measurement past present and future challenges
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Global Precipitation Measurement: Past, present, and future challenges. Chris Kidd ESSIC,University of Maryland, and NASA/Goddard Space Flight Center, USA. Goddard Space Flight Center. ESSIC/UMD 6 February 2012. Overview. Why precipitation? - The Value of Water - Facts and figures

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Global Precipitation Measurement: Past, present, and future challenges

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Global Precipitation Measurement:Past, present, and future challenges

Chris Kidd

ESSIC,University of Maryland, and

NASA/Goddard Space Flight Center, USA


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Overview

Why precipitation?

- The Value of Water

- Facts and figures

Precipitation Measurement

- Surface measurements

- Satellite retrievals

- Validation & inter-comparison studies

Challenges

- Characterisation of precipitation

- Mapping and integration


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Background

The King’s School, Grantham

(Maths, Physics, Geography)

University of Nottingham

(Geography – Cartography & Earth Obs.)

University of Bristol

(PMW retrievals of precipitation over land)

USRA - NASA/GSFC

(inter-comparisons & merged products)

University of Birmingham

(Satellite meteorology and climatology)

ESSIC - NASA/GSFC

(multi-source/scale precipitation products)


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Why precipitation?

“Our knowledge of the time and space distributions of rainfall,soil moisture, ground water recharge, and evapotranspirationare remarkably inadequate,in part because historical data bases are point measurements from which we have attempted extrapolation to large-scale fields.”

P.299 National Research Council (1991)

Opportunities in the hydrologic sciences, National Academy Press

“…critical atmospheric variables not adequately measured by current or planned systems [include] precipitation.”

UK Met Office. NERC CEOI Workshop, 13/11/09

Personally – it combines geography & weather

Precipitation is ultimately the input for all hydrological systems


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Precipitation – the enigma

Quantifying precipitation, its accuracies and errors is extremely problematic; critical issues affecting and influencing the observation and measurement of precipitation are:

i) the characteristics of the phenomenon being observed;

ii) the observational capability of the sensor;

iii) the interpretation of the observations and the derived parameters, and;

iv) the perceived versus real requirements of the subsequent applications.

Defining the accuracy and associated errors of any precipitation observation or measurement is therefore a multidimensional and inexact problem.


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Water factoids

1 mm per square metre = 1 litre (or 1 kg)

1 mm per square kilometre = 1,000,000 litres or 1000 tonnes

so, D.C. has ~1000 mm/yr ≡ 1,000,000 tonnes/yr/km2

Currently fresh water costs ~$2 per cubic metre, globally precipitation ‘contributes’ $258 trillion annually.

Over the US alone, precipitation is ‘worth’ $13 trillion annually.

“More than 2.8 billion people in 48 countries will face water stress or scarcity conditions by 2025.” WaterFootprint.org & WWF


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Water – facts & figures


2008 floods

2009 floods

UK Lake District floods 2008 & 2009


Surface measurements

Clee Hill radars

(C-band vs ATC)

Micro rain radar

0.2 mm/tip ARG100 gauge

0.1mm/tip Young’s Gauge


20,000

Rain

gauges

Radarduplicates rain-gauge coverage

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Conventional Observations

Precipitation is highly variable both temporally and spatially.

Measurements need to be representative


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Variance explained by nearest station

Jürgen Grieser

NOTE: Monthly data: shorter periods = lower explained variance


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

What is truth? Co-located 8 gauges / 4 MRRs


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Precipitation accumulation


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Surface measurements summary

Representativeness of surface measurements:

  • Over land generally good, but variable

  • Over oceans: virtually none-existent

    Measurement issues:

  • Physical collection – interferes with measurement (e.g. wind effects – frozen precip, etc)

  • Radar – imprecise backscatter:rainfall relationship (also clutter, range effects, bright band, etc)

Satellites offer consistent, regular measurements,

global coverage, real-time delivery of data


Satellite precipitation observation capabilities

1959 Vanguard 2

1960 TIROS-1

1966 ATS-1

1974 SMS-1

1978 SMMR

1983 NOAA-8

1987 SSM/I

1988 WetNet

1997 TRMM

1989 AIP-1

1991 AIP-2

1994 AIP-3

1990 PIP-1

1993 PIP-2

1996 PIP-3

1998 AMSU

2002 MSG

2003 SSM/IS

2001 IPWG

2006 Cloudsat

2004 PEHRPP

2011 Megha-Tropiques

2014 GPM

?

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

1960

Visible

1970

Infrared

1980

Passive Microwave

1990

2000

Active MW

2010

2020


Visible (including near IR)

  • Reflectance, cloud top properties (size, phase)

Infrared

  • Thermal emission – cloud top temperatures → height

Passive Microwave

  • Natural emissions from surface and precipitation (emission and scattering)

Active Microwave

  • Backscatter from precipitation particles

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Satellite retrieval of precipitation

Note: Observations are not direct measurements


Combine directness of MW observations with the resolution/frequency of IR observations

Calibration of Vis/IR-derived properties with microwave observations

Advect microwave estimates with information from IR observations

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Combined Vis/IR & microwave techniques

Vis/IR

Microwave (active/passive)

Rationale: Observation of cloud top properties (temperature/size), but indirect

Rationale: Observations more directly related to hydrometeors

Observations:Frequent observations (30mins); Good spatial resolution (1-4 km)

Observations:Infrequent observations (2/sat/day); Poor spatial resolution (5-25 km)

+ model information....


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

PM-IR products

‘Global’ <30 minute <12km rainfall estimatespossible

Infrared

daily

estimate

Passive

microwave

daily

estimate

Regionally

Calibrated

product

Can we generate 1km, 1min global estimates?


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Advection/Morphing products

12 May 2003

MSG – SSMI

study

Wind vectors derived from MSG 15 minutes data

(simple correlation match)

PMW estimates advected using MSG wind vectors: 0745-0930

Basis of ‘CMORPH’ and GSMaP techniques

uses forwards and backward propagation of PM rainfall


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Precipitation product inter-comparisons


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

NASA WetNet: Tallahassee c.1989


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

NASA WetNet PIP-1 Bristol c.1991


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

GPCP AIP-3 Shinfield Park c.1993


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

IPWG#4 CMA Beijing 2008


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

International Precipitation Working Group

Near real-time inter-comparison of model & satellite estimates vs radar/gauge


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

IPWG European Inter-comparison


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

IPWG: European region 07/11-01/12

Correlation

July August September October November December January

Satellites ~same as models in summer; models better in winter


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Radar vs gauge data

Radar (daily integrated)

Gauge data


Type of cloud/

rain

Movement:

Is the movement perpendicular or along the rain band?

Intensity

What is the range of values within the rain area?

Sensor field-of-view

Size/variability

What is the size and variability of the rain area(s)?

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Statistics: blame it on the weather!

Statistical success has as much to do with meteorology as the algorithms ability…


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

3-hourly/0.25 degree data availability

NOTE: not all ‘data’ is real ‘data’


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Precipitation Products, Europe 2009

MWCOMB

CMORPH

ECMWF

PERSIANN

3B42RT

GPCC gauge

2009 Annual Mean

mm/day

0 1 2 3 4 5 6 7 8 9 10

Orography & high latitudes still presents a challenge to retrieval techniques


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Precipitation Products, Africa 2009

10

9

8

7

6

5

4

3

2

1

0

CMORPH

NRLBLD

ECMWF

2009 Annual Mean

mmd-1

PERSIANN

3B42RT

GPCC gauge

Over central Africa: PMW overestimates (convective); gauges underestimate (representativeness); model ~right?


Agusti-Panareda and

Beljaars (2008)

30% satellite :

gauge difference

Extra-

Tropical

60°N-60°S

limit max.

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Retrieval challenges

Land-only 20W-20E latitudinal profile

- Good agreement in Tropics

- Poorer in the extra-Tropics


Bias-ratio

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Correlation

2005-11


Timeline

position

evaluation of

individual 0.25°

x 0.25° boxes

improving performance

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

SE England analysis (vs radar)


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Diurnal statistical performance (JJA)

09-12

09-12

12-15

12-15

12-15

12-15

Generated from 3-hourly accumulations

ECMWF: evident diurnal cycle in performance

CMORPH: over Germany performance in JJA ≈ that of ECMWF

Temporal/spatial analysis can help identify surface/satellite errors more easily


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Future Challenges I

Characterisation of precipitation

High latitude retrievals

  • Light precipitation

  • Snowfall and mixed-phase precipitation

  • Land/ocean/coastline consistency

    The retrieval of precipitation at higher latitudes is more challenging due to the physically diverse nature of the weather systems and backgrounds.


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

High-latitude processes

Cryospheric processes are complex with longer time-scale water cycle implications than the Tropics


Validation instrumentation at high latitudes to observe and measure precipitation

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

High latitude precipitation


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Model vs satellite

ECMWF

3B42RT

3-hourly precipitation accumulations for 1 June 2007

Clear differences between identification (or definition) of precipitation


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Distribution of light precipitation

  • Light rainfall becomes increasingly important towards the polar regions

  • COADS data shows light precipitation occurrence >80%; ~50% in mid-latitudes

  • European radar suggests ~85% of precipitation <1 mmh-1 (35%<0.1 mmh-1)

  • Accumulation of light precipitation is smaller, particularly in the Tropics

Current satellite techniques do not retrieve light precipitation well


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

LPVEx: 21 September 2010

Aranda

Rainrate

Rainrate

Jarvenpaa

AMSR

V10 rain

10:23Z

MRR data

Significant coastline problems and light-rain detection.


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

LPVEx: 14 October 2010

Rainrate

Jarvenpaa

MRR data

AMSR

V10 rain

10:29Z


falling snow

falling snow

surface snow

surface snow

SSMIS F17 0557Z 6 February 2012

Western Europe 5-6 February 2012

H150 183±1 183±3 183±6

SSMIS F17 0610Z 5 February 2012

H150 183±1 183±3 183±6

Fallen snow and falling snow do not necessarily have unique signatures


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Future Challenges II

Mapping & integration of data

  • Representation/mapping of data globally- in particular beyond the local areas or outside the tropics

  • Utilisation of multi-scale data sets– how to integrate different data sets to improve products

  • Sub-pixel resolution requirements – migration from coarse-resolution to fine-resolution products (both for actual precipitation products & processing)


75N 0.259

60N 0.500

45N 0.707

30N 0.866

Equator 1.000

30S 0.866

45S 0.707

60S 0.500

75S 0.259

Scale relative to the Equator (=1.00)

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Mapping

How should data be mapped?

Should data be mapped at all?

‘Standard’ mapping for global precipitation is the lat/lon (CED) grid: advantages include simplicity, ease of use and interpretation, but the main disadvantage is the non-equal area nature of the mapping, particularly at higher latitudes.

Critical for regions outside the tropics: at 60°N/S the E-W distance is 0.5 that of the N-S distance


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Local area mapping

Rationale: polar coverage and finer resolutions necessitate the generation of products of equal area.

Method: provide local-area mapping of products.

  • this should not take any more processing time

  • observations are mapped to each tile (via look-up-table)

  • each region has a small overlap with the neighbouring tile to allow consistency

  • motion vectors and products are generated for each tile

  • products are saved as lat/lon/value.


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Local area mapping errors

0 5 50 km error


Advection techniques

CMORPH motion vectors, 2.5° resolution


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Resolution

Can we adequately resolve the peculiarities associated with the movement of precipitation?

At present we only have very crude representations of precipitation motion.


Himalayas

Occurrence of rainfall

Annual total rainfall

Western Ghats

Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

High resolution climatologies

TRMM PR data: 13 years (1997→) at ~5 km resolution.

Rainfall shows significant local variability linked with relief.


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

Conclusions

Sound foundations;

  • 50+ years since the dawn of the satellite era

  • 33 years since usable observations for precipitation

  • 15 years since ‘first’ precipitation mission

Significant progress in precipitation retrievals;

  • coarse resolution through to ~0.25° 3-hourly estimates

  • - although generally limited to 60°N-60°S

Current & future issues;

  • High latitudes to complete global precipitation estimates

  • Fine resolution, multi-source retrievals - globally


Goddard Space

Flight Center

ESSIC/UMD 6 February 2012

The ultimate goal...

ECMWF operational model, annual precipitation 2009


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