JCSDA 2015 Summer Colloquium

A Study of Land Surface Emissivity for Microwave Precipitation Retrieval

Yaoyao Zheng, School of Meteorology, University of Oklahoma2015/08/06

Pierre-E Kirstetter, NOAA/National Severe Storms LaboratoryYang Hong, CEEs University of Oklahoma

Joseph Turk, Jet Propulsion Laboratory Berry Wen, NASA

past Bachelor in atmospheric science in Nanjing University of Information Science&

Technology (former Nanjing Institute of Meteorology)

2009Sep -2013June

Stratosphere-troposphere atmospheric process and their impact to atmospheric constituents in mesoscale weather system (Cloudsat)

Carbon nitrogen land-gas exchange in watershed of terrestrial ecosystem

Diagnostic study of binary typhoons using COMRPH, FY-2E and other synoptic data;

Master in Meteorology in University of Oklahoma

2013 Aug-present

Parallel computing: compare different versions of block cyclic reduction algorithm in adjoint method

Ground validation of TRMM precipitation Radar

QPE (quantitative precipitation estimates)

Remote sensing (Satellite and Radar)

Microwave radiometer (TMI/GMI)

Precipitation retrieval with microwave imager

F(measured emissivity)=F(emissivity from

heterogeneous)+F(emissivity from land surface/ocean)

Land surface emissivity: high and heterogeneous Ocean surface emissivity: small and uniform

Microwave radiometer

Microwave dielectric property

large contrast btw dielectric constant of dry matter and water

L-band, dielectric constant water ~80, dry soil ~3

Increase of water content in subsurface layer can increase dielectric constant, and lower microwave emissivity, and a lower brightness temperature

what is bound water and free water

Complexity of land surface emissivity Classification of land surface—based on MODIS and TRMM

Bare-ground, cropland, forest, grassland, inland water, shrub-land, urban area, woodland

Complexity of land surface emissivity Three main aspects to be considered

Soil moisture, soil texture, soil bulk density

Skin temperature(soil temperature or vegetation temperature), surface roughness, topography

vegetation effect-biomass, leaf area, vegetation structure, vegetation height, vegetation convergence, vegetation water content, intercepted water after rain or irrigation.

Frequency dependent (long wavelength vs short wavelength; skin depth or depth of view; almost unity emissivity in shallow layer dry out quickly after heavy precipitation)

Within-footprint spatial heterogeneity (4.5-5km)

Seasonal variation (bareground-forest/snow/)

Diurnal variation (soil moisture profiles is more uniform in early morning )

Microwave polarization difference index Theoretic basis: absolute magnitude of soil emissivity is lower at H

polarization, the sensitivity to changes in surface moisture is greater than V polarization

MPDI=(Tb_V-Tb_H)/(Tb_V+Tb_H)

The advantage of MPDI than other polarization difference, is that MPDI eliminates atmospheric effect through the differencing in numerator and cancel the surface temperature effect through the rationing at low frequencies.

high MPDI: water bodies, deserts, moist soil with no vegetation

Low MPDI: rough dry soil surfaces, dense vegetation

Data and idea

How much the index—MPDI can represent the surface land microwave properties and then we can remove part of precipitation estimation error due to the spatial and temporal variation of land surface emissivity affected by three main factors

Data from 2011 March-Oct TRMM (Precipitation Radar-PR + Microwave Imager-TMI), using NOAA/NSSL Multi-Radar Multi-Sensor high resolution (2min, 0.01 degree )precipitation products( ground-based radar + rain gauge) as reference

Categorize the non-raining pixels and raining pixels from match-ups pixels

Calculate MPDI and compare them in non-raining and raining conditions

TMI (9channels: 10Ghz-H,V;19GHz-H,V;222GHz H; 37GHz H-V; 85 GHz V-H)

MPDI in 4 frequencies, in raining/non-raning conditions over 9 types of land cover

Figure 1. raining and non-raining MPDI comparison over 10,19,37 and 85 Ghz in April

Figure 2. raining and non-raining MPDI comparison over 10,19,37 and 85 GHz in Sept

Sensitivity of MPDI to Microwave wavelength

Fig 3 comparison of MPDI over 4 frequencies: from cropland, forest, grassland, shrubland, woodland

Fig. 4 Comparison of MPDI over 4 frequencies: from bareground, urban, inland water/ocean

Rain fraction: indicating sub-pixel heterogeneity The percentage of area covered by precipitation in a pixel

Fig 5. MPDI probability density against rain fraction in July 2011 over 9 types of land cover for 4 frequencies

Consider two overpass over the same area, one for a precipitation event and the moment instantly after the rainAssumed the land surface properties do not change significantly after the rain

Fig 6.Time series of MPDI in raining event and non-raining moments immediately after raining

Fig 6. spatial distribution of MPDI in raining events and adjoint non-raining moments

future

For this research

Find good quality soil moisture/skin temperature/vegetation index to investigate the effect of surface skin temperature, vegetation effect, soil moisture on the surface emissivity

Establish a quantitative function to represent the MPDI in terms of surface skin temperature, vegetation and soil moisture

Use MPDI as a conditioning factor to adjust TMI brightness temperature and then conduct rainfall date analysis using this modified TMI brightness temperature

Research opportunities on PhD

Thanks for JCSDA and CIRA

Any comments ?