Modelo:

Actualizado:

hourly to monthly from 1980 to last month

Tiempo medio de Greenwich:

12:00 UTC = 06:00 MGZ

Resolutión:

0.5° x 0.65°

Parámetro:

Soaring Index

Descripción:

The Soaring Index map - updated every 6 hours - shows the modelled lift rate by thermals (convective clouds). The index is based on weather information between 5 000 feet (1 524 metres) and 20 000 feet (6 096 metres) and is expressed in Kelvin.
Table 1: Characteristic values for Soaring Index for soaring

Soaring Index	Soaring Conditions
Below -10   -10 to 5   5 to 20   Above 20	Poor   Moderate   Good   Excellent*

Table 2: Critical values for the Soaring Index

Soaring Index	Convective potential
15-20	Isolated showers, 20% risk for thunderstorms
20-25	Occasionally showers, 20-40% risk for thunderstorms
25-30	Frequent showers, 40-60% risk for thunderstorms.
30-35	60-80% risk for thunderstorms.
35 +	>80% risk for thunderstorms

MERRA:

The MERRA time period covers the modern era of remotely sensed data, from 1979 through the present, and the special focus of the atmospheric assimilation is the hydrological cycle. Previous long-term reanalyses of the Earth's climate had high levels of uncertainty in precipitation and inter-annual variability. The GEOS-5 data assimilation system used for MERRA implements Incremental Analysis Updates (IAU) to slowly adjust the model states toward the observed state. The water cycle benefits as unrealistic spin down is minimized. In addition, the model physical parameterizations have been tested and evaluated in a data assimilation context, which also reduces the shock of adjusting the model system. Land surface processes are modeled with the state-of-the-art GEOS-5 Catchment hydrology land surface model. MERRA thus makes significant advances in the representation of the water cycle in reanalyses.

Reanalyse:

Retrospective-analyses (or reanalyses) integrate a variety of observing systems with numerical models to produce a temporally and spatially consistent synthesis of observations and analyses of variables not easily observed. The breadth of variables, as well as observational influence, make reanalyses ideal for investigating climate variability. The Modern Era-Retrospective Analysis for Research and Applications supports NASA's Earth science objectives, by applying the state-of-the-art GEOS-5 data assimilation system that includes many modern observing systems (such as EOS) in a climate framework.