The Pathfinder Atmospheres–Extended AVHRR Climate Dataset

Heidinger, A. K.; Foster, Michael J.; Walther, A.; Zhao, Xuepeng

doi:10.1175/bams-d-12-00246.1

Cited by 222 publications

(181 citation statements)

References 35 publications

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“…2c). The modeled monthly mean cloud-free area is somewhat larger than the one obtained from the PATMOS-x satellite data (Heidinger et al, 2014). The cloud fraction in the model is particularly larger than observed in regions where precipitation is simulated, but the overall bias is negative with −13 % (−27 %) in September and −5 % (−7 %) in December.…”

Section: Sensitivity Studiesmentioning

confidence: 62%

The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling study

et al. 2015

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Abstract. South Africa has one of the largest industrialized economies in Africa. Emissions of air pollutants are particularly high in the Johannesburg-Pretoria metropolitan area, the Mpumalanga Highveld and the Vaal Triangle, resulting in local air pollution. This study presents and evaluates a setup for conducting modeling experiments over southern Africa with the Weather Research and Forecasting model including chemistry and aerosols (WRF-Chem), and analyzes the contribution of anthropogenic emissions to the total black carbon (BC) concentrations from September to December 2010.The modeled BC concentrations are compared with measurements obtained at the Welgegund station situated ca. 100 km southwest of Johannesburg. An evaluation of WRF-Chem with observational data from ground-based measurement stations, radiosondes, and satellites shows that the meteorology is modeled mostly reasonably well, but precipitation amounts are widely overestimated and the onset of the wet season is modeled approximately 1 month too early in 2010. Modeled daily mean BC concentrations show a temporal correlation of 0.66 with measurements, but the total BC concentration is underestimated in the model by up to 50 %.Sensitivity studies with anthropogenic emissions of BC and co-emitted species turned off show that anthropogenic sources can contribute up to 100 % to BC concentrations in the industrialized and urban areas, and anthropogenic BC and co-emitted species together can contribute up to 60 % to PM 1 levels. Particularly the co-emitted species contribute significantly to the aerosol optical depth (AOD). Furthermore, in areas of large-scale biomass-burning atmospheric heating rates are increased through absorption by BC up to an altitude of about 600 hPa.

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Section: Sensitivity Studiesmentioning

confidence: 62%

The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling study

et al. 2015

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“…Stable BT records are also needed for users interested in direct radiance assimilation (such as the NOAA National Centers for Environmental Prediction, NCEP, and the NASA Global Model Assimilation Office, GMAO). Furthermore, they will also benefit reprocessing of other Earth environmental variables including the cloud products and cloud mask, radiation budget, land and ice surface temperatures (e.g., [26,27], and references herein). Improved real time AVHRR L1b processing is also needed for many real-time applications.…”

Section: Discussionmentioning

confidence: 99%

Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands

Ignatov

Kihai

et al. 2016

Remote Sensing

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Abstract:Recently, the National Oceanic and Atmospheric Administration (NOAA) performed sea surface temperature (SST) reanalysis (RAN1) from seven AVHRR/3s onboard NOAA-15 to -19 and Metop-A and -B, from 2002-present. Operational L1b data were used as input. The time series of clear-sky ocean brightness temperatures (BTs) and derived SSTs were found to be unstable. The SSTs were empirically stabilized against in situ SSTs using a 90-day moving filter, while the measured BTs were left intact. However, some users are interested in direct radiance assimilation and need stable BTs. Additionally, stabilized BTs will greatly benefit SST (by minimizing the need for their empirical stabilization), and other Level 2 products derived from AVHRR. To better understand the AVHRR calibration and stabilize its BTs, the Sensor Stability for SST (3S; www.star.nesdis.noaa.gov/sod/sst/3s/) system was established at NOAA, which monitors orbital statistics of the sensor measured blackbody temperatures (BBTs), blackbody counts (BCs), and the space counts (SCs), along with the derived calibration gains and offsets. Analyses are performed separately for the satellite night (when the satellite is in the Earth's shadow) and day (on the sunlit part of its orbit). Factors affecting the BBT, BC and SC are also monitored, including the Sun and Moon position relative to the sensor, local equator crossing time, and duration of the satellite night. All AVHRRs show long-term and band-specific smooth changes in the calibration gains and offsets, which are occasionally perturbed by spurious non-monotonic anomalies. The most prominent irregularities occur shortly after the satellite crosses from the night into day, or when it is in a (near) full Sun orbit for extended periods of time. We argue that the operational quality control (QC) and calibration procedures are suboptimal and should be improved. Analyses in 3S suggest that a more stringent QC is needed, and scan lines where the calibration coefficients cannot be derived, due to poor quality SC, BC or BBT data, should be filled in by interpolation from the best parts of orbit or more broadly satellite lifetime. Work is underway to redesign the AVHRR QC and calibration algorithms and create a more stable long-term record of AVHRR calibration and BTs, and use them in the subsequent SST RANs.

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“…Our second condition for solution (verification) however raises very interesting questions. The recent report by Norris et al, [55] examines a 30-year record of P CT -τ C from ISCCP and PATMOS-x [56] compared to similarly processed model statistics. They report statistically significant spatial shifts in cloud occurrence as well as changes to the vertical distribution of cloudiness (Fig.…”

Section: High Cloudsmentioning

confidence: 99%

Using Active Remote Sensing to Evaluate Cloud-Climate Feedbacks: a Review and a Look to the Future

Mace

Berry

2017

Curr Clim Change Rep

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Uncertainty in the equilibrium climate sensitivity (ECS) of the Earth continues to be large. Aspects of the cloud feedback problem have been identified as fundamental to the uncertainty in ECS. Recent analyses have shown that changes to cloud forcing with climate change can be decomposed into contributions from changes in cloud occurrence that are proportional to globally averaged temperature change and changes associated with rapid adjustments in the system that are independent of changes to globally averaged surface temperature. Together these responses enhance warming due to (1) cloud feedback from increasing cloud altitude by upper tropospheric clouds and (2) decreases in cloud coverage by marine boundary layer clouds. We argue that active remote sensing from space can play a unique and crucial role in constraining our understanding of these separate phenomena. For 1, the feedback associated with changing tropical cirrus is predicted to emerge from the statistical noise of the climate system within the next one to two decades. However, active remote sensing will need to continue for that signal to be observed since accurate placement of these clouds in the vertical dimension is necessary. For 2, the processes associated with changes to marine boundary layer clouds have been linked to the coupling between cloud and precipitation microphysics and air motions over remote ocean basins where precipitation formation in shallow convection is modulated by changes to aerosols and thermodynamics. Exploiting the synergy in combined active and passive remote sensing is likely one of the only ways of constraining our evolving theoretical understanding of low-level cloud processes as represented in cloudresolving models and for validating global-scale models.

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The Pathfinder Atmospheres–Extended AVHRR Climate Dataset

Cited by 222 publications

References 35 publications

The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling study

The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling study

Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands

Using Active Remote Sensing to Evaluate Cloud-Climate Feedbacks: a Review and a Look to the Future

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