Validation of satellite-based sea surface temperature products against in situ observations off the western coast of Sumatra

This study validated the sea surface temperature (SST) datasets from the Group for High-Resolution SST Multi Product Ensemble (GMPE), National Oceanic and Atmospheric Administration (NOAA) Optimal Interpolation (OI) SST version 2 and 2.1 (OIv2 and OIv2.1), and Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) in the area off the western coast of Sumatra against in situ observations. Furthermore, the root mean square differences (RMSDs) of OIv2, OIv2.1, and ECCO2 were investigated with respect to GMPE, whose small RMSD < 0.2 K against in situ observations confirmed its suitability as a reference. Although OIv2 showed a large RMSD (1–1.5 K) with a significant negative bias, OIv2.1 (RMSD < 0.4 K) improved remarkably. In the average SST distributions for December 2017, the differences among the 4 datasets were significant in the areas off the western coast of Sumatra, along the southern coast of Java, and in the Indonesian inland sea. These results were consistent with the ensemble spread distribution obtained with GMPE. The large RMSDs of OIv2 corresponded to high clouds, and it was suggested that the change in the satellites used for SST estimation contributed to the improvement in OIv2.1.

Locations for the best lidar view on mid-level and high clouds

&lt;p&gt;Mid-level altocumuls clouds (Ac) and high cirrus clouds (Ci) can be considered as natural observatories for studying cloud glaciation in the atmosphere. While their altitude makes them difficult to access with in-situ instruments, they can be conveniently observed from ground with active remote-sensing instruments such as lidar and radar. However, active remote sensing of Ac and Ci at visible wavelengths with lidar requires a clear line of sight between the instrument and the target cloud. It is therefore advisable to carefully assess potential locations for deploying ground-based lidar instruments in field experiments or for long-term observations that are focussed on mid-level or high clouds. Here, observations of clouds with two spaceborne lidars are used to assess where ground-based lidar measurements of mid- and upper level clouds are least affected by the light-attenuating effect of low-level clouds. It is found that cirrus can be best observed in the tropics, the Tibetan plateau, the western part of North America, the Atacama region, the southern tip of South America, Greenland, Antarctica, and parts of Western Europe. For the observation of altocumuls clouds, a ground-based lidar is best placed on Greenland, Antarctica, the western flank of the Andes and Rocky Mountains, the Amazon, central Asia, Siberia, Western Australia, or the southern half of Africa.&lt;/p&gt;

Statistical Characteristics of Cloud Heights over Lanzhou, China from Multiple Years of Micro-Pulse Lidar Observation

The macroscopic characteristics of clouds over Lanzhou, China were investigated using micro-pulse lidar data from September 2005 to November 2011. The results show that the mean of the cloud base height, cloud peak height, cloud top height and cloud thickness during the observation was 4.03 km, 4.81 km, 5.50 km and 1.47 km, respectively; the maximum frequency of the cloud base height, cloud peak height, cloud top height and cloud thickness was 25.7% in the range of 1–2 km, 16.2% in the range of 2–3 km, 14.6% in the range of 2–3 km and 42.2% in the range of 1–2 km, respectively; the maximum frequency of cloud base height was 24.2%, 24.6%, 29.7% and 21.4% in spring, summer, autumn and winter, respectively, all in the range of 1–2 km, and middle clouds occurred most frequently at 41.4%, followed by low clouds (33.7%) and high clouds (24.9%) during the observation period; the maximum frequency of cloud peak height was 15.8% in the range of 3–4 km, 18% in the range of 4–5 km, 20% in the range of 2–3 km in autumn and 18.6% in the range of 5–6 km in winter; the maximum frequency of cloud top height was 14% in the range of 3–4 km in spring, 16% in the range of 4–5 km in summer, 20.1% in the range of 2–3 km in autumn and 17.8% in the range of 7–8 km in winter; the maximum frequency of cloud thickness was 44.9%, 35.6% and 52% in the range of 1–2 km in spring, summer and winter, respectively, while it was 44.9% in the range of 0–1 km in autumn; the cloud thickness was mostly less than 3 km; generally, the thicker of cloud, the less the frequency.

Examining Cloud Macrophysical Changes over the Pacific for 2007–2017 Using CALIPSO, CloudSat, and MODIS Observations

Cloud macrophysical changes over the Pacific from 2007 to 2017 are examined by combining CALIOP and CloudSat (CALCS) active-sensor measurements, and these are compared with MODIS passive-sensor observations. Both CALCS and MODIS capture well-known features of cloud changes over the Pacific associated with meteorological conditions during El Niño-Southern Oscillation (ENSO) events. For example, mid (cloud tops at 3–10 km) and high (cloud tops at 10–18 km) cloud amounts increase with relative humidity (RH) anomalies. However, a better correlation is obtained between CALCS cloud volume and RH anomalies, confirming more accurate CALCS cloud boundaries than MODIS. Both CALCS and MODIS show that low cloud (cloud tops at 0–3 km) amounts increase with EIS and decrease with SST over the eastern Pacific, consistent with earlier studies. It is also further shown that the low cloud amounts do not increase with positive EIS anomalies if SST anomalies are positive. While similar features are found between CALCS and MODIS low cloud anomalies, differences also exist. First, compared to CALCS, MODIS shows stronger anti-correlation between low and mid/high cloud anomalies over the central and western Pacific, which is largely due to the limitation in detecting overlapping clouds from passive MODIS measurements. Second, compared to CALCS, MODIS shows smaller impacts of mid and high clouds on the low troposphere (< 3 km). The differences are due to the underestimation of MODIS cloud layer thicknesses of mid and high clouds.

Validation of Satellite-Based Sea Surface Temperature Products Against in Situ Observations Off the Western Coast of Sumatra

This study validated the sea surface temperature (SST) datasets from the Group for High-Resolution SST Multi Product Ensemble (GMPE), National Oceanic and Atmospheric Administration (NOAA) Optimal Interpolation (OI) SST version 2 and 2.1 (OIv2 and OIv2.1), and Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) in the area off the western coast of Sumatra against in situ observations. Furthermore, the root mean square differences (RMSDs) of OIv2, OIv2.1, and ECCO2 were investigated with respect to GMPE, whose small RMSD < 0.2 K against in situ observations confirmed its suitability as a reference. Although OIv2 showed a large RMSD (1-1.5 K) with a significant negative bias, OIv2.1 (RMSD < 0.4 K) improved remarkably. In the average SST distributions for December 2017, the differences among the 4 datasets were significant in the areas off the western coast of Sumatra, along the southern coast of Java, and in the Indonesian inland sea. These results were consistent with the ensemble spread distribution obtained with GMPE. The large RMSDs of OIv2 corresponded to high clouds, and it was suggested that the change in the satellites used for SST estimation contributed to the improvement in OIv2.1.

EFFECT OF THIN HIGH CLOUDS AND AEROSOL LAYERS ON THE HEATING AND DISSIPATION OF LOW-LEVEL CLOUDS IN THE ARCTIC

Based on the radiative transfer simulation and analysis of observational data on cloudiness, temperature, and humidity in the Arctic atmosphere in the years of the increased ice melting, a hypothesis is proposed on the effect of thin high clouds and aerosol layers on the heating and dissipation of low-level clouds in the Arctic. Along with the effect of thin high scattering layers on the transmission of solar radiation by tropospheric clouds, the dissipation of low-level clouds in the years of the increased ice melting can be one of the main mechanisms of the natural warming in the Arctic.