The daily average surface temperature (LST) obtained from the following locations is crucial for various applications, such as global and regional climate change analysis, for polar orbiting spacecraft. However, polar orbiting spacecraft can only effectively sample the surface in very limited circumstances, under cloudless conditions, on a daily basis. These limitations result in systematic sampling bias (Δ Tsb) on the daily average LST (Tdm) estimated using traditional methods that directly use clear sky LST observations as Tdm. Several methods have been proposed for estimating Tdm, but they are becoming increasingly rare and can produce spatiotemporal seamless Tdmacross the globe. Based on MODIS and reanalysis data, we propose an improved annual and diurnal temperature cycle based framework (referred to as the IADTC framework) to generate global spatiotemporal seamless Tdmproducts ranging from 2003 to 2019 (named the GADTC product).
The LST product of MODIS and the MERRA2 (Modern Retrospective Analysis Research and Application Version 2) reanalysis dataset need to be used as input data for the IADTC framework. We also used in-situ LST measurements of SURFRED and FLUXNET to validate the IADTC framework and GADTC products. MODIS' LST products, including MOD11C1 and MYD11C1 LST products, were included in the sixth collection from 2003 to 2019 (available in https://ladsweb.nascom.nasa.gov/ get. Surface air temperature (SAT) is used to drive the reconstruction of cloud based LST in ATC models, from https://disc.gsfc.nasa.gov/datasets/M2I1NXLFO_V5.12.4/summary Obtain
The OADTC framework includes two steps for generating Tdm: (1) using the ATC model to reconstruct the instantaneous cloud LST, ensuring that there are four valid LSTs at the four transit times each day; (2) Simulate day night LST dynamics using a four parameter DTC model and estimate Tdm. This study improved the OADTC framework using more advanced ATC models and optimized the estimation of Tdmusing the DTC model. The use of this improved framework (known as the IADTC framework) to generate global seamless Tdm involves four steps: data preprocessing, reconstruction of cloud LST using advanced ATC models, linear interpolation of MODIS transit time, and estimation of Tdmusing DTC models.
Verification shows that the IADTC framework significantly reduces systematic Δ Tdm. Only in-situ data was used for validation, and the evaluation showed that the mean absolute value and mean deviation for SURFRED and FLUXNET data were close to zero. The direct comparison between GADTC products and in-situ measurements shows that the MAEs of SURFRED and FLUXNET datasets are 2.2 and 3.1 K, respectively, with average deviations of -1.6 and -1.5K. Further analysis using GADTC products as a reference shows that Tdmusing traditional averaging methods produces positive systems with Δ Tsbgreater than 2.0 K in low and mid latitude regions, while in high latitude regions. Although the global average LST trend (from 2003 to 2019) was calculated using traditional methods, the IADTC framework was relatively closed (all between 0.025 and 0.029 Kyr-1), and regional differences in LST trends did indeed occur in LST. The pixel based MAE trend falls between these two methods, reaching 0.012 Kyr-1. We believe that the IADTC framework can guide further optimization of Tdmglobal estimation, and the generated GADTC products should be valuable in various applications, such as global and regional warming analysis.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB6441.2024
10.5281/zenodo.6287052
