Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: Tosiyuki Nakaegawa x
  • Refine by Access: All Content x
Clear All Modify Search
Tosiyuki Nakaegawa

Abstract

Land cover classification is a fundamental and vital activity that is helpful for understanding natural dynamics and the human impacts of land surface processes. Available multiple 1-km global land cover datasets have been compared to identify classification accuracy and uncertainties for vegetation land cover types, but they have not been adequately compared for water-related land cover types. Six 1-km global land cover datasets were comprehensively examined by focusing on three water-related land cover types (snow and ice, wetlands, and open water). The global mean per-pixel agreement measured by the class-specific consistency is high for snow and ice, medium for open water, and low for wetlands. The agreement is low for snow and ice in low latitudes and high for open water and snow and ice in high latitudes. Areas classified as wetlands in a pixel in one dataset are rarely classified as wetlands in the same pixel in the other five datasets. These areas are most often classified as forest, wetland, or shrub. Areas of snow and ice and open water in some regions are not always chronologically consistent among the datasets because nonsatellite data and different algorithms are used to determine the areas. Further research is necessary to reduce uncertainty in the water-related land cover classification and to develop an advanced classification algorithm that can detect water under a vegetation canopy for improvement in wetland classification. Chronological inconsistency between 1-km land cover datasets and satellite observation periods must also be addressed.

Full access
Tosiyuki Nakaegawa and Masao Kanamitsu

Abstract

Cluster analysis was used to study seasonal forecast skills of the winter season NCEP seasonal forecast model (SFM) hindcasts over the Pacific–North America (PNA) sector. Two skill scores based on cluster mean and ensemble mean are compared. It was shown that the anomaly correlation coefficients (ACCs) of cluster mean are generally higher than those of the simple ensemble mean. The results indicated that the skill was affected by the existence of multiple atmospheric regimes. Multiple regimes tend to appear more often in near-normal tropical Pacific sea surface temperature (SST) episodes, while a single regime tends to appear during warm/cold episodes. The dissimilarity among the cluster members is small and the number of the dominant cluster members is also small when the tropical SST anomaly is large, suggesting that the external forcing reduces the frequency of occurrence of the multiple regimes. The ACC improvements from the ensemble mean ACCs to the cluster mean ACCs are statistically significant. Thus, the cluster mean can be used as a supplementary tool for seasonal forecasting.

Full access
Tosiyuki Nakaegawa, Masao Kanamitsu, and Thomas M. Smith

Abstract

This study addresses the interdecadal trend in potential skill score as estimated from the 500-hPa height temporal correlation coefficient (TCC), based on a 50-yr 10-member ensemble GCM integration with observed SST. The skill scores are based on the perfect model assumption, in which one of the members of the ensemble is assumed to be true. A distinct decadal positive trend in the TCC in boreal winter (December–January–February) was found. This trend is shown to be consistent with the positive trend in the interdecadal time-scale temporal variance of SST. The geographical pattern of the differences of the TCC between each decade and the 50-yr period resembles the Matsuno–Gill pattern, suggesting that the increase in the TCC is due to the Rossby wave excitation induced by the anomalous diabatic heating caused by the anomalous SST. Similar interdecadal trends in the variance of the Southern Oscillation index and Pacific–North American pattern were found in both the observation and the simulation. The interdecadal trend in the variance of 500-hPa geopotential height over the continental United States, however, existed only in the simulation.

Full access
Tosiyuki Nakaegawa, Osamu Arakawa, and Kenji Kamiguchi

Abstract

The present study investigated the onset and withdrawal dates of the rainy season in Panama by using newly developed, gridded, daily precipitation datasets with a high horizontal resolution of 0.05° based on ground precipitation observations. The onset and withdrawal dates showed very complicated geographical features, although the country of Panama is oriented parallel to latitude lines, and the geographical patterns of the onset and withdrawal dates could simply reflect the latitudinal migration of the intertropical convergence zone, as seen in other regions and countries. An absolute threshold value of 3 mm day−1 (pentad mean precipitation) was used to determine the onset and withdrawal dates. The onset and withdrawal dates obtained from the gridded daily precipitation dataset clearly depicted the migration of the rainy season. The rainy season starts suddenly in pentad 21 (11–15 April) in most of eastern Panama and in pentad 22 (16–20 April) in most of western Panama. The termination of the rainy season begins in Los Santos Province during pentad 67 (27 November–1 December) and expands to both the eastern and western surrounding areas. There is no dry season in the western part of the Caribbean coastal zone. Water vapor fluxes and topography suggest dynamical causes, such as a topographically induced upward mass flux accompanied by high humidity, for the complicated geographical features of the onset and withdrawal dates. An assessment was made of uncertainties in the timing of the onset and withdrawal associated with the definition of these terms.

Full access