The Ensemble Mars Atmosphere Reanalysis System (EMARS) dataset version 1.0 contains hourly gridded atmospheric variables for the planet Mars, spanning Mars Year (MY) 24 through 33 (1999 through 2017). A reanalysis represents the best estimate of the state of the atmosphere by combining observations that are sparse in space and time with a dynamical model and weighting them by their uncertainties. EMARS uses the Local Ensemble Transform Kalman Filter (LETKF) for data assimilation with the GFDL/NASA Mars Global Climate Model (MGCM). Observations that are assimilated include the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) temperature retrievals. The dataset includes gridded fields of temperature, wind, surface pressure, as well as dust, water ice, CO2 surface ice and other atmospheric quantities. Reanalyses are useful for both science and engineering studies, including investigations of transient eddies, the polar vortex, thermal tides and dust storms, and during spacecraft operations.

A B S T R A C T Effective simulation of diurnal variability is an important aspect of many geophysical data assimilation systems. For the Martian atmosphere, thermal tides are particularly prominent and contribute much to the Martian atmospheric circulation, dynamics and dust transport. To study the Mars diurnal variability and Mars thermal tides, the Geophysical Fluid Dynamics Laboratory Mars Global Climate Model with the 4D-local ensemble transform Kalman filter (4D-LETKF) is used to perform an analysis assimilating spacecraft temperature retrievals. We find that the use of a 'traditional' 6-hr assimilation cycle induces spurious forcing of a resonantly enhanced semi-diurnal Kelvin waves represented in both surface pressure and mid-level temperature by forming a wave 4 pattern in the diurnal averaged analysis increment that acts as a 'topographic' stationary forcing. Different assimilation window lengths in the 4D-LETKF are introduced to remove the artificially induced resonance. It is found that short assimilation window lengths not only remove the spurious resonance, but also push the migrating semi-diurnal temperature variation at 50 Pa closer to the estimated 'true' tides even in the absence of a radiatively active water ice cloud parameterisation. In order to compare the performance of different assimilation window lengths, short-term to mid-range forecasts based on the hour 00 and 12 assimilation are evaluated and compared. Results show that during Northern Hemisphere summer, it is not the assimilation window length, but the radiatively active water ice clouds that influence the model prediction. A 'diurnal bias correction' that includes bias correction fields dependent on the local time is shown to effectively reduce the forecast root mean square differences between forecasts and observations, compensate for the absence of water ice cloud parameterisation and enhance Martian atmosphere prediction. The implications of these results for data assimilation in the Earth's atmosphere are discussed.

The claim for a zonal-dipole structure in interannual variations of the tropical Indian Ocean (IO) SSTs-the Indian Ocean dipole (IOD)-is reexamined after accounting for El Niño-Southern Oscillation's (ENSO) influence. The authors seek an a priori accounting of ENSO's seasonally stratified influence on IO SSTs and evaluate the basis of the related dipole mode index, instead of seeking a posteriori adjustments to this index, as common.Scant observational evidence is found for zonal-dipole SST variations after removal of ENSO's influence from IO SSTs: The IOD poles are essentially uncorrelated in the ENSO-filtered SSTs in both recent and century-long (1900-2007) periods, leading to the breakdown of zonal-dipole structure in surface temperature variability; this finding does not depend on the subtleties in estimation of ENSO's influence. Deconstruction of the fall 1994 and 1997 SST anomalies led to their reclassification, with a weak IOD in 1994 and none in 1997.Regressions of the eastern IOD pole on upper-ocean heat content, however, do exhibit a zonal-dipole structure but with the western pole in the central-equatorial IO, suggesting that internally generated basin variability can have zonal-dipole structure at the subsurface.The IO SST variability was analyzed using the extended-EOF technique, after removing the influence of Pacific SSTs; the technique targets spatial and temporal recurrence and extracts modes (rather than patterns) of variability. This spatiotemporal analysis also does not support the existence of zonal-dipole variability at the surface. However, the analysis did yield a dipole-like structure in the meridional direction in boreal fall/ winter, when it resembles the subtropical IOD pattern (but not the evolution time scale).

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The retrieval of MTSAT multi-spectral satellite image rainfall intensity field was studied, with which the "Unit-Feature Spatial Classification (UFSC) method" was proposed to become the foremost basis of the possibility of continuous observation of real-time precipitation from geostationary satellite. In this method, MTSAT multi-spectral satellite measured value and measured precipitation rate from high density ground stations of plum rain season in east china (Jiangsu Province, Zhejiang Province and Anhui Province) in 2007 are combined to conduct the cooperative analysis, and therefore the distribution features of the level of each precipitation probability and each precipitation intensity are well established on different two-dimensional and three-dimensional spectral feature spaces. On the basis, the discrimination matrices, correspondingly, are established for precipitation probability and precipitation intensity of different spectral combinations. Different spectral combinations are used for the construction of the discrimination matrices of the day and the night, respectively. For the day, IR1 (11µm), IR3 (6.7µm), VIS (0.7µm), IR12 (T IR2-IR1 ) and IR13 (T IR3-IR1 ) are available, among which IR1, VIS and IR3 (or IR13) are mainly used ; for the night, IR1, IR3, IR4 (3.7µm), IR12, IR13, IR14 (T IR4-IR1 )and IR24 (T IR4-IR2 ) are available and IR1, IR3 and IR24 (or IR14) are mainly used. The contrast test between the observed data of precipitation and the retrieval results based on precipitation data from basic stations and reference stations in China in 2007 shows that, 30% precipitation probability can ideally distinguish precipitation area from non-precipitation area; and the analysis of precipitation intensity category also matches well with the fact. It is well known that the observation of satellite is instantaneous one time per hour while the rain gauge observation is an accumulative process during an hour. The error study further suggests that the difference between the two observation methods is the vital cause of the relative error.