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Satellite Observations Of Rainfall And Water Vapor
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Satellite Observations Of Rainfall And Water Vapor


Talk presented at Davidson College March 10, 2011. Comprises an introduction to and summary of my thesis research.

Talk presented at Davidson College March 10, 2011. Comprises an introduction to and summary of my thesis research.

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  • 1. Satellite Observations of Rainfall and Water Vapor Kelly Howell with Tom Vonder Haar and Stan Kidder Cooperative Institute for Research in the Atmosphere March 10, 2011
  • 2. Measured Quantities
    • Total precipitable water (TPW) is the total atmospheric water vapor contained in a vertical column (e.g., mm)
    • Rainfall rate (RR) is the amount of rain that falls in a given period of time (e.g., mm hr -1 )
      • A RR threshold of 0.1 mm hr -1 is used in this study to differentiate between rain and no-rain scenarios
  • 3. September 5, 2008 at 00 UTC Areas with elevated TPW are often associated with instances of rainfall. Hurricane Ike
  • 4. Motivation
    • Use new datasets to create total precipitable water (TPW) and rainfall rate (RR) climatologies
    • Further investigate the relationship between TPW and RR with the hopes of improving rain forecasting techniques:
      • In areas lacking adequate forecasting capabilities
      • In areas that may experience flood-inducing rainfall
    • This research was supported by the DoD Center for Geosciences/Atmospheric Research at CSU under Cooperative Agreement W911NF-06-2-0015 with the Army Research Laboratory
  • 5. Global Circulation and Rain Production
  • 6. General Circulation Patterns (a) Tropical atmosphere expands, polar atmosphere contracts, causing air to flow poleward at the upper levels (b) Redistribution of mass causes high pressure at poles, low pressure in tropics, causing equatorward flow at the surface (c) Coriolis force imparts a westward component to the equatorward flow and an eastward component to the poleward flow (d) Spontaneous instability imparts a wave-like character to the eastward flow in the midlatitudes Source: Wallace, J. M. and P. V. Hobbs, 2006: Atmospheric Science: An Introductory Survey. Elsevier Inc. , 483 pp.
  • 7. Angular Momentum When an air parcel moves closer to the axis of rotation (i.e., poleward), the relative eastward velocity must increase to conserve angular momentum When an air parcel moves farther from the axis of rotation (i.e., equatorward), the relative westward velocity must increase to conserve angular momentum
  • 8. Surface Pressure
    • Poleward, warm flow in western oceans
    • Equatorward, cool flow in eastern oceans
    High Surface Pressure Associated with sinking motion and export of water vapor Low Surface Pressure Associated with rising motion and import of water vapor Source: Wallace, J. M. and P. V. Hobbs, 2006: Atmospheric Science: An Introductory Survey. Elsevier Inc. , 483 pp.
  • 9. September 5, 2008 at 00 UTC Oceanic Deserts Midlatitudes Subtropics Tropics Subtropics Midlatitudes West Pacific Warm Pool SPCZ ITCZ
  • 10. Vertical Structure
    • All weather happens in the troposphere, the lowest 10 km of the atmosphere
    • Temperature generally decreases with height, at an average rate of 6.5 K km -1
    • Water vapor concentrations are higher at the surface
    Source: Wallace, J. M. and P. V. Hobbs, 2006: Atmospheric Science: An Introductory Survey. Elsevier Inc. , 483 pp.
  • 11. Cloud Formation
    • A warm, moist surface air parcel rises due to its relative buoyancy.
    • As the parcel rises, it cools, bringing its vapor pressure closer to saturation
    Sources: http://asd-www.larc.nasa.gov/edu_act/clouds.html, Wallace, J. M. and P. V. Hobbs, 2006: Atmospheric Science: An Introductory Survey. Elsevier Inc. , 483 pp. Condensation<Evaporation Condensation=Evaporation p=ρRT Vapor pressure: the pressure exerted by water vapor on its environment Cooler air requires less water vapor to reach saturation, by the Clausius-Clapeyron equation.
  • 12. Types of Rainfall
    • Convective
      • A buoyant warm surface air parcel cools as it rises to reach saturation
      • Rain falls from typically deep clouds at various intensities
    • Stratiform
      • Warm air flows over colder surface to reach saturation
      • Rain falls from shallow, low clouds at typically low intensities
    Sources: http://www.csulb.edu/~rodrigue/geog140/lectures/humidity.html, http://www.djkennedy.com/gallery/2077194_3Csku/1/107032825_Erp4B/Medium
  • 13. Factors Contributing to Rain Production
    • Sufficient atmospheric water vapor
    • Rising motion
    • Horizontal flow dynamics (moisture convergence)
      • Less than 20% of the precipitation that falls comes from evaporation within a distance of about 1000 km
      • Local evaporation will not affect the intensity of the precipitation as much as the moisture that is transported into a region
  • 14. Data
  • 15. bTPW Dataset
    • bTPW: Cooperative Institute for Research in the Atmosphere (CIRA) blended total precipitable water product
    • Uses measurements at 19, 22, 23, 31, and 37 GHz from 6 instruments (Advanced Microwave Sounding Unit-A2 (AMSU-A2) on 3 satellites, Special Sensor Microwave Imager (SSM/I) in 3 other satellites)
    • This dataset provides TPW estimates between 71 o N and 71 o S, 16 km resolution at the equator
    • Formed by compositing data from the 6 instruments over 12 hours
  • 16. CMORPH Dataset
    • CMORPH: Climate Prediction Center (CPC) morphing method
    • Uses measurements at 10, 19, 21, 22, 37, 85, 89, 150, and 183 GHz from 7 instruments (Special Sensor Microwave Imager (SSM/I) on 3 satellites, Advanced Microwave Sounding Unit-B (AMSU-B) on 3 satellites, TRMM Microwave Imager (TMI) on 1 satellite).
    • This dataset provides rainfall rate estimates between 60 o N and 60 o S at 0.25 o ×0.25 o resolution
    • Missing data is ‘filled in’ using an infrared cloud advection technique
  • 17. Study Domain
    • Spatially, the data cover 60 o N to 60 o S for all longitudes at 0.25 o ×0.25 o resolution.
    • CMORPH covers the entire domain; bTPW covers only the oceans. Over the continental US, bTPW is supplemented by GPS TPW estimates.
    • Temporally, both datasets were sampled 6-hourly from February 2006 to December 2008 and used at 00, 06, 12, and 18 UTC.
    • In all, 3928 time periods were used in this analysis, or approximately 92% of the temporal domain.
    • If one dataset was ‘missing’ during a time period, that day was excluded from the study in order to avoid any diurnal bias.
  • 18. Why use satellites?
    • Rain gauges allow for direct measurements of rainfall and rainfall rate, but for a very small area (~10 -1 m 2 )
    • Radars also allow for more direct measurement of an area’s rainfall, and on a larger scale (~10 6 -10 8 m 2 ). But most are stationary and thus can only sample a local area.
    • Although measurements from satellites introduce more sources of error, they allow for nearly global sampling of the atmosphere, at reasonable time and spatial scales.
      • In this study, almost all of the scanning area is sampled at least once a day
    Source: Kidder, S. Q., and T. H. Vonder Haar, 1995: Satellite Meteorology: An Introduction. Academic Press , 456 pp. What a typical scan pattern might look like
  • 19. Radiation
    • Blackbody radiation depends on temperature and wavelength
    • In the microwave region, by the Rayleigh-Jeans approximation, radiance is proportional to temperature; this is called the ‘brightness temperature’.
    • Emissivity ε : the ability of an object to radiate energy relative to a blackbody at the same temperature.
      • ε blackbody : 1.0
      • ε non-blackbody : 0 to 1
  • 20. Radiation Along a Path Material absorbs radiation Material emits radiation Other radiation is scattered into path Material scatters radiation out of path
  • 21. Why use microwave radiation?
    • Visible and infrared radiation cannot penetrate through clouds, so we use longer microwave radiation, which can.
    • Microwave radiation in this study is in the range:
    • 10.7– 183.3 GHz, or
    • 28.04 – 1.64 mm
    Source: Kidder, S. Q., and T. H. Vonder Haar, 1995: Satellite Meteorology: An Introduction. Academic Press , 456 pp. 19 GHz: window region 22 GHz: sensitive to water vapor 37 GHz: attenuated by clouds and rain drops >85 GHz: sensitive to ice
  • 22. Detecting Total Precipitable Water Ocean ε ~ 0.5 (‘cold’ background) Low, constant emissivity Variable amounts of water vapor are easily detectable Land ε ~ 0.9 (‘warm’ background) High, highly variable emissivity Variable amounts of water vapor are harder to detect
    • Over the ocean, water vapor increases the brightness temperature above what it would be from a cold ocean background alone.
    • The bTPW dataset is mostly over the ocean for this reason.
    0.5 T B (cold) 1.0 T B (warm) 1.0 T B (warm) 0.9 T B (warm)
  • 23. Detecting Rainfall
    • Over the ocean, emission from rain drops acts to increase the brightness temperature above the surface emission temperature.
    • Over land surfaces, the scattering of precipitation-sized ice acts oppositely, lowering the brightness temperature below what would have been observed for a land background alone.
    Ocean ε ~ 0.5 (‘cold’ background) Low, constant emissivity Radiation emitted from rain drops and scattered from ice particles is detected Land ε ~ 0.9 (‘warm’ background) High, highly variable emissivity Radiation scattered from ice particles is detected 0.5 T B (cold) 0.9 T B (warm) 1.0 T B (warm) scattering (cools) scattering (cools)
  • 24. Total Precipitable Water and Rainfall Climatology
  • 25. Annual Mean TPW The global mean TPW is 24.94 mm, with a maximum of approximately 45 mm just north of the equator.
  • 26. Seasonal Mean TPW The seasonal TPW distributions are similar to previous findings, with the SPCZ extending its farthest eastward during DJF and TPW highs around southeast Asia during the JJA monsoon period. The presence of a double ITCZ can be detected in the eastern tropical Pacific during MAM.
  • 27. Annual Mean RR in mm day -1 Source: Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc. , 78 , 2539-2558. Surface Type (CMORPH) RR [mm day -1 ] Ocean 2.68 Land 2.57 All Surfaces 2.63 Surface Type (CMAP) RR [mm day -1 ] Ocean 3.02 Land 1.86 Globe 2.69
  • 28. Annual Mean RR in mm year -1 The zonal mean RR distribution over land is fairly symmetric, while the oceanic distribution peaks in the tropics and has secondary peaks in the midlatitudes. Latitude (degrees N)
  • 29. Annual Mean Rainfall Frequency The zonal distributions of rainfall frequency are analogous to the zonal distributions of rainfall. Overall, the oceans receive rain more frequently than land surfaces, but land surfaces receive heavy rain more frequently than ocean surfaces. Latitude (degrees N) Rainfall frequency = 100× 0.07 % 0.03 % 10.0 mm hr -1 0.34 % 0.27 % 5.0 mm hr -1 RR Threshold 8.58 % 11.04 % 0.1 mm hr -1 Land Ocean Surface Type
  • 30. Seasonal Mean RR in mm month -1 Seasonal mean rainfall estimates from CMORPH are similar to previous findings (Ferraro et al. 1996). The NH and SH land areas show large changes between DJF and JJA: the winter hemisphere’s land areas receive very little rainfall.
  • 31. Seasonal Mean Rainfall Frequency Rainfall frequency maximizes in the expected areas: the ITCZ, SPCZ, and eastern storm tracks. Rain falls infrequently in desert regions. Using the 0.1 mm hr -1 threshold, quasi-global frequencies hover around 10%. Season 0.1 mm hr -1 0.5 mm hr -1 1.0 mm hr -1 DJF 10.31 % 4.88 % 2.76 % MAM 10.30 % 4.79 % 2.76 % JJA 10.17 % 5.00 % 2.92 % SON 10.09 % 4.95 % 2.88 %
  • 32. Regional Studies
  • 33. TPW Threshold for Rainfall? Time series of (a) RR in mm hr -1 , (b) TPW in mm, and (c) global solar radiation at the Koto Tabang GPS station on Sumatra Island during JJA 2001. Rainfall does not tend to occur at times with relatively low TPW. Source: Wu, P., J.-I. Hamada, S. Mori, Y. I. Tauhid, M. D. Yamanaka, and F. Kimura, 2003: Diurnal variation of precipitable water over a mountainous area of Sumatra Island. J. Appl. Meteorol. , 42 , 1107-1115.
  • 34. Regional Studies The following plots were constructed using the data at each of these grid points analyzed over all 35 months. Location Longitude Latitude East of Florida 70 o W 30 o N Indian Ocean 75 o E 8 o S East of Japan 142 o E 35 o N South of Panama 83 o W 4 o N South Atlantic Ocean 22 o W 45 o S North Atlantic Ocean 30 o W 50 o N West Pacific 155 o E 6 o N Southeastern Pacific 120 o W 8 o S SPCZ 170 o E 10 o S
  • 35. TPW Distributions Annual mean TPW is highly variable in the midlatitude location, relatively high in the tropical location, and relatively low in the oceanic desert location. TPW (mm) Location Mean TPW East of Florida 30.66 mm West Pacific 54.28 mm Southeastern Pacific 29.62 mm
  • 36. RR Distributions Equation of exponential decay. A more negative slope indicates a faster rate of decay (i.e., there are relatively few heavy rain events). More negative slopes tend to be associated with lower rainfall frequencies. RR [mm hr -1 ] Location Slope of Fit Rainfall Frequency East of Florida -0.58 11.11 % West Pacific -0.516 31.20 % Southeastern Pacific -1.556 2.29 %
  • 37. RR vs TPW The shapes of these distributions approximate the TPW distributions. In general, the higher RRs occur at the more frequently occurring TPW values. However, this is not the case in the southeastern Pacific, where the highest RRs occur at higher TPW values. TPW (mm)
  • 38. Probability of Rainfall RR ≥ 0.1 mm hr -1 RR ≥ 3.0 mm hr -1 Higher-intensity rainfall is less likely at lower TPW values. TPW (mm)
  • 39. Global RR Distributions by TPW Range
  • 40. RR Distribution by TPW Range At higher TPW values, rainfall is more probable and there is a higher proportion of heavier rainfall. RR [mm hr -1 ] TPW Range Probability of Rainfall 0-15 mm 2.34 % 15-30 mm 7.64 % 30-45 mm 16.59 % 45-60 mm 29.89 % 60-75 mm 45.89 %
  • 41. RR Distribution by TPW Range The RR distributions are not strictly exponential, but an exponential fit is a consistent representation of the distribution. Steeper slopes are associated with drier environments. RR [mm hr -1 ] TPW Range Slope of RR Distribution 0-15 mm -1.01 15-30 mm -0.90 30-45 mm -0.70 45-60 mm -0.59 60-75 mm -0.49
  • 42. Conclusions
    • The bTPW and CMORPH datasets result in climatologies that are comparable to those from previous studies. TPW, rainfall, and rainfall frequency are the highest in the ITCZ, SPCZ, and west Pacific warm pool, and the lowest in oceanic desert regions.
    • The ocean receives rainfall in greater quantities and more frequently compared with land surfaces, but land surfaces receive heavier rainfall more frequently.
    • A RR distribution forms an approximate exponenetial decay, with the slope of the exponential fit related to the frequency of the rainfall, with more negative slopes being associated with lower rainfall frequency
    • Even at very high TPW values, the probability of rainfall is only about 50 %. Relatively high TPW is only one factor in the production of rain.
    • In a global mean sense, higher TPW values are associated with more rainfall and higher RRs. But because dynamical patterns are so critical to rainfall production, using TPW to forecast rainfall is only appropriate when done for a particular region since this study does not correct for dynamical variability.
  • 43. Future Work
    • Extend this study over land areas when an accurate TPW dataset over land becomes available.
    • Incorporate CloudSat estimates into the RR results in order to better measure lighter rainfall events. This is especially important in the oceanic desert regions.
    • Because TPW may not be detected when rain is present, estimate missing the TPW values in order to create more robust statistics and to observe single events.
    • Use TPW anomaly data to compare with occurrences of rainfall.
    • Investigate the RR distributions in different TPW regimes for particular locations, instead of globally.
    • Incorporate the RR and TPW distributions into forecast and climate models.
  • 44. Questions?
  • 45.