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Global Precipitation Measurement: Past, present, and future challenges

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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  1. Global Precipitation Measurement:Past, present, and future challenges Chris Kidd ESSIC,University of Maryland, and NASA/Goddard Space Flight Center, USA

  2. 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

  3. 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)

  4. 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

  5. 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.

  6. 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

  7. Goddard Space Flight Center ESSIC/UMD 6 February 2012 Water – facts & figures

  8. 2008 floods 2009 floods UK Lake District floods 2008 & 2009

  9. Surface measurements Clee Hill radars (C-band vs ATC) Micro rain radar 0.2 mm/tip ARG100 gauge 0.1mm/tip Young’s Gauge

  10. 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

  11. Goddard Space Flight Center ESSIC/UMD 6 February 2012 Variance explained by nearest station Jürgen Grieser NOTE: Monthly data: shorter periods = lower explained variance

  12. Goddard Space Flight Center ESSIC/UMD 6 February 2012 What is truth? Co-located 8 gauges / 4 MRRs

  13. Goddard Space Flight Center ESSIC/UMD 6 February 2012 Precipitation accumulation

  14. 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

  15. 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

  16. 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

  17. 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....

  18. 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?

  19. 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

  20. Goddard Space Flight Center ESSIC/UMD 6 February 2012 Precipitation product inter-comparisons

  21. Goddard Space Flight Center ESSIC/UMD 6 February 2012 NASA WetNet: Tallahassee c.1989

  22. Goddard Space Flight Center ESSIC/UMD 6 February 2012 NASA WetNet PIP-1 Bristol c.1991

  23. Goddard Space Flight Center ESSIC/UMD 6 February 2012 GPCP AIP-3 Shinfield Park c.1993

  24. Goddard Space Flight Center ESSIC/UMD 6 February 2012 IPWG#4 CMA Beijing 2008

  25. 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

  26. Goddard Space Flight Center ESSIC/UMD 6 February 2012 IPWG European Inter-comparison

  27. 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

  28. Goddard Space Flight Center ESSIC/UMD 6 February 2012 Radar vs gauge data Radar (daily integrated) Gauge data

  29. 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…

  30. Goddard Space Flight Center ESSIC/UMD 6 February 2012 3-hourly/0.25 degree data availability NOTE: not all ‘data’ is real ‘data’

  31. 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

  32. 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?

  33. 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

  34. Bias-ratio Goddard Space Flight Center ESSIC/UMD 6 February 2012 Correlation 2005-11

  35. 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)

  36. 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

  37. 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.

  38. 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

  39. Validation instrumentation at high latitudes to observe and measure precipitation Goddard Space Flight Center ESSIC/UMD 6 February 2012 High latitude precipitation

  40. 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

  41. 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

  42. 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.

  43. Goddard Space Flight Center ESSIC/UMD 6 February 2012 LPVEx: 14 October 2010 Rainrate Jarvenpaa MRR data AMSR V10 rain 10:29Z

  44. 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

  45. 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)

  46. 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

  47. 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.

  48. Goddard Space Flight Center ESSIC/UMD 6 February 2012 Local area mapping errors 0 5 50 km error

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