On the Lessons Learned from the Operations of the ERBE Nonscanner Instrument in Space and the Production of the Nonscanner TOA Radiation Budget Dataset.

Monitoring the flow of radiative energy at top-of-atmosphere (TOA) is essential for understanding the Earth's climate and how it is changing with time. The determination of TOA global net radiation budget using broadband nonscanner instruments has received renewed interest recently due to advances in both instrument technology and the availability of small satellite platforms. The use of such instruments for monitoring Earth's radiation budget was attempted in the past from satellite missions such as the Nimbus 7 and the Earth Radiation Budget Experiment (ERBE). This paper discusses the important lessons learned from the operation of the ERBE nonscanner instrument and the production of the ERBE nonscanner TOA radiation budget data set that have direct relevance to current nonscanner instrument efforts.

Determination of CERES TOA fluxes using Machine learning algorithms. Part I: Classification and retrieval of CERES cloudy and clear scenes.

Continuous monitoring of the Earth radiation budget (ERB) is critical to our understanding of the Earth's climate and its variability with time. The Clouds and the Earth's Radiant Energy System (CERES) instrument is able to provide a long record of ERB for such scientific studies. This manuscript, which is first of a two-part paper, describes the new CERES algorithm for improving the clear/cloudy scene classification without the use of coincident cloud imager data. This new CERES algorithm is based on a subset of modern artificial intelligence (AI) paradigm called Machine Learning (ML) algorithms. This paper describes development and application of the ML algorithm known as Random Forests (RF) which is used to classify CERES broadband footprint measurements into clear and cloudy scenes. Results from the RF analysis carried using the CERES Single Scanner Footprint (SSF) data for the months of January and July are presented in the manuscript. The daytime RF misclassification rate (MCR) shows relatively large values (>30%) for snow, sea ice and bright desert surface types while lower values of (<10%) for forest surface type. MCR values observed for the nighttime data in general show relatively larger values for most of the surface types compared to the daytime MCR values. The modified MCR values show lower values (< 4%) for most surface types after thin cloud data is excluded from the analysis. Sensitivity analysis shows that the number of input variables and decision trees used in the RF analysis has substantial influence in determining the classification error.

Changes in Earth's albedo measured by satellite.

NASA global satellite data provide observations of Earth's albedo, i.e., the fraction of incident solar radiation that is reflected back to space. The satellite data show that the last four years are within natural variability and fail to confirm the 6% relative increase in albedo inferred from observations of earthshine from the moon. Longer global satellite records will be required to discern climate trends in Earth's albedo.

Evidence for large decadal variability in the tropical mean radiative energy budget.

It is widely assumed that variations in Earth's radiative energy budget at large time and space scales are small. We present new evidence from a compilation of over two decades of accurate satellite data that the top-of-atmosphere (TOA) tropical radiative energy budget is much more dynamic and variable than previously thought. Results indicate that the radiation budget changes are caused by changes in tropical mean cloudiness. The results of several current climate model simulations fail to predict this large observed variation in tropical energy budget. The missing variability in the models highlights the critical need to improve cloud modeling in the tropics so that prediction of tropical climate on interannual and decadal time scales can be improved.