SUMMARYThis paper investigates daytime convective development over land and its representation in single-column models (SCMs) and cloud-resolving models (CRMs). A model intercomparison case is developed based on observations of the diurnal cycle and convection during the rainy season in Amazonia. The focus is on the 6 h period between sunrise and early afternoon which was identified in previous studies as critical for the diurnal cycle over summertime continents in numerical weather prediction and climate models. This period is characterized by the formation and growth of a well-mixed convective boundary layer from the early morning temperature and moisture profiles as the surface sensible-and latent-heat fluxes increase after sunrise. It proceeds with the formation of shallow convective clouds as the convective boundary layer deepens, and leads to the eventual transition from shallow to deep precipitating convection around local noon. To provide a benchmark for other models, a custom-designed set of simulations, applying increasing in time computational domain and decreasing spatial resolution, was executed. The SCMs reproduced the previously identified problem with premature development of deep convection, less than two hours after sunrise. The benchmark simulations suggest a possible route to improve SCMs by considering a time-evolving cumulus entrainment rate as convection evolves from shallow to deep and the cloud width increases up to an order of magnitude. The CRMs featuring horizontal grid length around 500 m are capable of capturing the qualitative aspects of the benchmark simulations, but there are significant differences among the models. Two-dimensional CRMs tend to simulate too rapid a transition from shallow to deep convection and too high a cloud cover.

Intercalibration of satellite instruments is critical for detection and quantification of changes in the Earth's environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be interoperable, the instruments must be cross-calibrated. To meet the stringent needs of such applications, instruments must provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d'unités traceable calibration and validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stability monitoring, and quality assurance need to be underpinned and evidenced by comparison with "peer instruments" and, ideally, highly calibrated in-orbit reference instruments. Intercalibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Intercalibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated intercalibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth's climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of postlaunch radiometric calibration of remote sensing satellite instruments through intercalibration.

NOAA, through the Joint Polar Satellite System (JPSS) program, in partnership with the National Aeronautical and Space Administration, launched the Suomi National Polar-orbiting Partnership (S-NPP) satellite, a risk reduction and data continuity mission, on 28 October 2011. The JPSS program is executing the S-NPP Calibration and Validation program to ensure that the data products comply with the requirements of the sponsoring agencies. The Ozone Mapping and Profiler Suite (OMPS) consists of two telescopes feeding three detectors measuring solar radiance scattered by the Earth's atmosphere directly and solar irradiance by using diffusers. The measurements are used to generate estimates of total column ozone and vertical ozone profiles for use in near-real-time applications and extension of ozone climate data records. The calibration and validation efforts are progressing well, and both Level 1 (Sensor Data Records) and Level 2 (Ozone Environmental Data Records) have advanced to release at Provisional Maturity. This paper provides information on the product performance over the first 22 months of the mission. The products are evaluated through the use of internal consistency analysis techniques and comparisons to other satellite instrument and ground-based products. The initial performance finds total ozone showing negative bias of 2 to 4% with respect to correlative products and ozone profiles often within ±5% in the middle and upper stratosphere of current operational products. Potential improvements in the measurements and algorithms are identified. These will be implemented in coming months to reduce the differences further.

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