Katsaros, K. B., A. Bentamy, M. Bourassa, N. Ebuchi, J. Gower, W. T. Liu, and S. Vignudelli. (2011). Climate Data Issues from an Oceanographic Remote Sensing Perspective. In Remote Sensing of the Changing Oceans (pp. 7–32). Berlin, Germany: Springer-Verlag.
Abstract: In this chapter we review several climatologically important variables
with a history of observation from spaceborne platforms. These include sea surface
temperature and wind vectors, altimetric estimates of sea surface height, energy and
water vapor fluxes at the sea surface, precipitation over the ocean, and ocean color.
We then discuss possible improvements in sampling for climate and climate change
definition. Issues of consistency of different data sources, archiving and distribution
of these types of data are discussed. The practical prospect of immediate international
coordination through the concept of virtual constellations is discussed and
applauded.
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Liu, Y., & Minnett, P. J. (2016). Sampling errors in satellite-derived infrared sea-surface temperatures. Part I: Global and regional MODIS fields. Remote Sensing of Environment, 177, 48–64.
Abstract: Long time series of accurate Sea Surface Temperatures (SSTs) are needed to resolve subtle signals that may be indicative of a changing climate. Motivated by the stringent requirements on SST accuracy required for Climate Data Records (CDR) we quantify sampling errors in satellite SSTs. Infrared sensors, including the Moderate Resolution Imaging Spectroradiometer (MODIS), have sampling errors caused by incomplete coverage primarily due to clouds and inter-swath gaps (gaps between successive swaths/orbits). Unlike retrieval errors, the sampling errors are introduced when calculating mean values and in generating gap-free SST fields. We generate MODIS-sampled SST fields by superimposing MODIS cloud masks on top of the Multi-scale Ultrahigh Resolution (MUR) SST field for the same day. Based on the MODIS-sampled fields, we calculate sampling errors at different temporal and spatial resolutions to examine the impacts at different scales. Our results indicate that sampling errors are significant, more so in the high latitudes, especially the Arctic. The 30 degrees N-30 degrees S zonal band is found to have the smallest errors; a notable exception is the persistent negative errors found in the Tropical Instability Wave area, where the mesoscale ocean-atmosphere interaction leads to a more frequently satellite sampling above the cold sections of the wave area. The global mean sampling error is generally positive and increases approximately exponentially with missing data fraction at a fixed averaging interval, while error variability is mainly controlled by SST variability. Areas with persistent cloud cover have large sampling errors in temporally averaged SSTs. We conclude that the sampling error can be an important or even dominant component of the error budget of mean and gap-free SST fields. Climate data generation and interpretation of satellite-derived SST CDRs and their application must be conducted with due regard to the sampling error.
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van Bussel, L. G. J., Ewert, F., Zhao, G., Hoffmann, H., Enders, A., Wallach, D., et al. (2016). Spatial sampling of weather data for regional crop yield simulations. Agricultural and Forest Meteorology, 220, 101–115.
Abstract: Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50,100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated.
The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.
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Zou, X., Weng, F., & Yang, H. (2014). Connecting the Time Series of Microwave Sounding Observations from AMSU to ATMS for Long-Term Monitoring of Climate. J. Atmos. Oceanic Technol., 31(10), 2206–2222.
Abstract: The measurements from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board NOAA polar-orbiting satellites have been extensively utilized for detecting atmospheric temperature trend during the last several decades. After the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite on 28 October 2011, MSU and AMSU-A time series will be overlapping with the Advanced Technology Microwave Sounder (ATMS) measurements. While ATMS inherited the central frequency and bandpass from most of AMSU-A sounding channels, its spatial resolution and noise features are, however, distinctly different from those of AMSU. In this study, the Backus–Gilbert method is used to optimally resample the ATMS data to AMSU-A fields of view (FOVs). The differences between the original and resampled ATMS data are demonstrated. By using the simultaneous nadir overpass (SNO) method, ATMS-resampled observations are collocated in space and time with AMSU-A data. The intersensor biases are then derived for each pair of ATMS–AMSU-A channels. It is shown that the brightness temperatures from ATMS now fall well within the AMSU data family after resampling and SNO cross calibration. Thus, the MSU–AMSU time series can be extended into future decades for more climate applications.
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