Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation

1994 ◽  
Vol 99 (C3) ◽  
pp. 5219 ◽  
Author(s):  
William J. Emery ◽  
Yunyue Yu ◽  
Gary A. Wick ◽  
Peter Schluessel ◽  
Richard W. Reynolds
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water

scholarly journals Seasonal Precipitation Predictability for the Northern Hemisphere Using Concurrent and Preseason Atmospheric Water Vapor Transport and Sea Surface Temperature

2021 ◽  
Vol 22 (1) ◽  
pp. 183-199
Author(s):  
Shida Gao ◽  
Pan Liu ◽  
Upmanu Lall
Keyword(s):  
Climate Change ◽  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Northern Hemisphere ◽  
Vapor Transport ◽  
Sea Surface ◽  
Water Vapor Transport ◽  
Seasonal Precipitation ◽  
Atmospheric Water

AbstractIntegrated atmospheric water vapor transport (IVT) is a determinant of global precipitation. In this paper, using the CERA-20C climate reanalysis dataset, we explore three questions in Northern Hemisphere precipitation for four seasons: 1) What is the covariability between the leading spatiotemporal modes of seasonal sea surface temperature (SST), the seasonal IVT, and the seasonal precipitation for the Northern Hemisphere? 2) How well can the leading spatial modes of seasonal precipitation be reconstructed from the leading modes of IVT and SST for the same season? 3) How well can the leading modes of precipitation for the next season be predicted from the leading modes of the current season’s SST and IVT? Wavelet analyses identify covariation in the leading modes of seasonal precipitation and those of IVT and SST in the 2–8-yr band, with the highest amplitude in the March–May (MAM) season, and provide a firm physical explanation for the potential predictability. We find that a subset of the 10 leading principal components of the seasonal IVT and SST fields has significant trends in connections with seasonal precipitation modes, and provides an accurate statistical concurrent reconstruction and one-season-ahead forecast of the leading seasonal precipitation modes, thus providing a pathway to improving the understanding and prediction of precipitation extremes in the context of climate change attribution, seasonal and longer prediction, and climate change scenarios. The same-season reconstruction model can explain 76% of the variance, and the next-season forecast model can explain 58% variance of hemispheric precipitation on average.

scholarly journals Variability of vertical structure of precipitation with sea surface temperature over the Arabian Sea and the Bay of Bengal as inferred by TRMM PR measurements

2018 ◽  
Author(s):  
Kadiri Saikranthi ◽  
Basivi Radhakrishna ◽  
Thota Narayana Rao ◽  
Sreedharan Krishnakumari Satheesh
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Vertical Structure ◽  
Tropical Rainfall Measuring Mission ◽  
Sea Surface ◽  
Cloud Formation ◽  
Freezing Level

Abstract. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25 reflectivity profiles data during the period 1998–2013 are used to study the differences in the vertical structure of precipitation and its variation with sea surface temperature (SST) over the Arabian Sea (AS) and the Bay of Bengal (BOB). Even though the AS and the BOB are parts of the Indian Ocean, they exhibit distinct features in vertical structure of precipitation and its variation with SST. The variation of reflectivity and precipitation echo top occurrence with SST is remarkable over the AS but trivial over the BOB. The median reflectivity increases with SST at all heights below 10 km altitude, but the increase is prominent below the freezing level height over the AS. On the other hand, irrespective of altitude, reflectivity profiles are same at all SSTs over the BOB. To understand these differences, variation of aerosols, cloud and water vapor with SST is studied over these seas. At SSTs less than 27 °C, the observed high aerosol optical depth (AOD) and low total column water vapor (TCWV) over the AS results in small Cloud effective radius (CER) values and low reflectivity. As SST increases AOD decreases and TCWV increases, which result in large CER and high reflectivity. Over the BOB the change in AOD, TCWV and CER with SST is marginal. Thus, the observed variations in reflectivity profiles seem to be present from the cloud formation stage itself over both the seas.

scholarly journals Improving Satellite-Derived Sea Surface Temperature Accuracies Using Water Vapor Profile Data

2011 ◽  
Vol 28 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Ian J. Barton
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Sea Surface ◽  
Radiative Transfer Model ◽  
Clear Correlation ◽  
Transfer Model ◽  
The Difference

Abstract Analyses based on atmospheric infrared radiative transfer simulations and collocated ship and satellite data are used to investigate whether knowledge of vertical atmospheric water vapor distributions can improve the accuracy of sea surface temperature (SST) estimates from satellite data. Initially, a simulated set of satellite brightness temperatures generated by a radiative transfer model with a large maritime radiosonde database was obtained. Simple linear SST algorithms are derived from this dataset, and these are then reapplied to the data to give simulated SST estimates and errors. The concept of water vapor weights is introduced in which a weight is a measure of the layer contribution to the difference between the surface temperature and that measured by the satellite. The weight of each atmospheric layer is defined as the layer water vapor amount multiplied by the difference between the SST and the midlayer temperature. Satellite-derived SST errors are then plotted against the difference in the sum of weights above an altitude of 2.5 km and that below. For the simple two-channel (with typical wavelengths of 11 and 12 μm) analysis, a clear correlation between the weights differences and the SST errors is found. A second group of analyses using ship-released radiosondes and satellite data also show a correlation between the SST errors and the weights differences. The analyses suggest that, for an SST derived using a simple two-channel algorithm, the accuracy may be improved if account is taken of the vertical distribution of water vapor above the ocean surface. For SST estimates derived using algorithms that include data from a 3.7-μm channel, there is no such correlation found.

Sign in / Sign up

Export Citation Format

Share Document