An Analysis of the Reliability of a New Dataset of Transmission Line Icing Thickness in Southern China

Based on ERA-Interim data, gauge observations, transmission line icing observational data, and hindcasted predictors from a numerical forecast system of transmission line icing, a new transmission line icing thickness (TLIT) dataset was constructed to solve the problem of limited historical data. The reliability of the dataset was analyzed using case studies and climate data. The results showed that the descriptions of three icing events in southern China by the TLIT were consistent with the actual observational data, and the icing thickness differences were less than 2 mm. The spatial distribution of annual icing days and icing thickness calculated using meteorological observation station icing data (OIT) and the TLIT data had a similar pattern, with small differences in the numerical values. A rotated empirical orthogonal function (REOF) decomposition was conducted for 67 transmission line icing events. It was found that the spatial distributions of the first three characteristic vectors of the TLIT and OIT data were similar, and the correlation coefficients for the time coefficients of the first three characteristic vectors were 0.801, −0.443, and 0.576, respectively. Three key areas were identified based on the first three patterns of REOF, and the average icing thickness of 67 events in southern China and the three key areas was calculated. The correlation coefficients of icing thickness calculated by the TLIT and OIT data for these areas were 0.648, 0.384, 0.565, and 0.599, respectively. The results illustrate that the TLIT data can reflect the temporal and spatial variations of ice thickness in southern China.

Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function

The Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA) satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF) and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections.

Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function

The AVHRRs instruments onboard the series of NOAA satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of sensors onboard. Depending on the amplitude of a diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to bracket an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in correcting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and their rigorous testing before applying final orbital drift corrections.

Bi-modal Structure and Variability of Large-Scale Diabatic Heating in the Tropics

Tropical diabatic heating profiles estimated using sounding data from eight field campaigns were diagnosed to document their common and prevailing structure and variability that are relevant to the large-scale circulation. The first two modes of a rotated empirical orthogonal function analysis—one deep, one shallow—explain 85% of the total variance of all data combined. These two modes were used to describe the heating evolution, which led to three composited heating profiles that are considered as prevailing large-scale heating structures. They are, respectively, shallow, bottom heavy (peak near 700 hPa); deep, middle heavy (peak near 400 hPa); and stratiform-like, top heavy (heating peak near 400 hPa and cooling peak near 700 hPa). The amplitudes and occurrence frequencies of the shallow, bottom-heavy heating profiles are comparable to those of the stratiform-like, top-heavy ones. The sequence of the most probable heating evolution is deep tropospheric cooling to bottom-heavy heating, to middle heavy heating, to stratiform-like heating, then back to deep tropospheric cooling. This heating transition appears to occur on different time scales. Each of the prevailing heating structures is interpreted as being composed of particular fractional populations of various types of precipitating cloud systems, which are viewed as the building blocks for the mean. A linear balanced model forced by the three prevailing heating profiles produces rich vertical structures in the circulation with multiple overturning cells, whose corresponding moisture convergence and surface wind fields are very sensitive to the heating structures.

The relationship between seasonal snow cover in Japan and recent climatic change

Atmospheric circulation patterns associated with snowfall fluctuations in Japan are examined using a rotated empirical orthogonal function (EOF) analysis. We also compute correlation coefficients between the scores of EOF modes in the 500 hPa geopotential height field of the Northern Hemisphere (NH) and amounts of snowfall in Japan on annual, monthly and pentad time scales. It is found that recent variability of snowfall amount in Japan is closely related to the long-term variations of large-scale circulation patterns. It is suggested that the dominance of teleconnection patterns such as Pacific/North American (PNA) and Northern Asian (NA) are responsible for the increase of snowfall in the coastal regions of the Sea of Japan during the cold period for Japan (1977–86).

The relationship between seasonal snow cover in Japan and recent climatic change

Atmospheric circulation patterns associated with snowfall fluctuations in Japan are examined using a rotated empirical orthogonal function (EOF) analysis. We also compute correlation coefficients between the scores of EOF modes in the 500 hPa geopotential height field of the Northern Hemisphere (NH) and amounts of snowfall in Japan on annual, monthly and pentad time scales. It is found that recent variability of snowfall amount in Japan is closely related to the long-term variations of large-scale circulation patterns. It is suggested that the dominance of teleconnection patterns such as Pacific/North American (PNA) and Northern Asian (NA) are responsible for the increase of snowfall in the coastal regions of the Sea of Japan during the cold period for Japan (1977–86).