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A dynamical–system approach is proposed to describe the relationship between precipitation and a chosen predictor. This is done by constructing a two dimensional phase space spanned by predictor and predictant. This study uses two sounding data sets from the Tropical Western Pacific and Friuli Venezia Giulia (FVG) over North–East Italy as representatives of the tropics and midlatitudes, in addition to a basin–scale average over the winter–period North Atlantic from global re–analysis data. In contrast to conventional correlation-based approaches, the proposed approach depicts periodic cycles, as well as discharge–recharge cycles as its nonlinear extension. Discharge–recharge cycles for tropical convection are identified by using both the convective available potential energy (CAPE) and the column–integrated water vapor (CIW) as predictors, as well as the baroclinicity for the winter–period North–Atlantic rain. On the other hand, the midlatitude rain, as seen over FVG as well as the winter–period North Atlantic, does not constitute a well–defined periodic cycle either with CAPE or CIW as a predictor. The inferred phase–space trajectories are more deterministic at peripheries of dense data areas rather than at a middle in the phase space. Data–dense areas in phase space, where traditional approaches primarily focus, are associated with more prediction uncertainties in our analysis due to more phase-velocity fluctuations.

A dynamical–system approach is proposed to describe the relationship between precipitation and a chosen predictor. This is done by constructing a two dimensional phase space spanned by predictor and predictant. This study uses two sounding data sets from the Tropical Western Pacific and Friuli Venezia Giulia (FVG) over North–East Italy as representatives of the tropics and midlatitudes, in addition to a basin–scale average over the winter–period North Atlantic from global re–analysis data. In contrast to conventional correlation-based approaches, the proposed approach depicts periodic cycles, as well as discharge–recharge cycles as its nonlinear extension. Discharge–recharge cycles for tropical convection are identified by using both the convective available potential energy (CAPE) and the column–integrated water vapor (CIW) as predictors, as well as the baroclinicity for the winter–period North–Atlantic rain. On the other hand, the midlatitude rain, as seen over FVG as well as the winter–period North Atlantic, does not constitute a well–defined periodic cycle either with CAPE or CIW as a predictor. The inferred phase–space trajectories are more deterministic at peripheries of dense data areas rather than at a middle in the phase space. Data–dense areas in phase space, where traditional approaches primarily focus, are associated with more prediction uncertainties in our analysis due to more phase-velocity fluctuations. [ABSTRACT FROM AUTHOR]

The atmospheric reanalysis datasets have been widely used to understand the variability of atmospheric water vapor on various temporal and spatial scales for climate change research. The difference among a variety of reanalysis datasets, however, causes the uncertainty of corresponding results. In this study, the climatology of atmospheric column-integrated water vapor for the period from 2000 to 2012 was compared among three latest third-generation atmospheric reanalyses including European Centre for Medium-range Weather Forecasts Interim Re-Analysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR), while possible explanation on the difference between them was given. The results show that there are significant differences among three datasets in the multi-year global distribution, variation of interannual cycle, long-term trend and so on, though high similarity for principal mode describing the variability of water vapor. Over oceans, the characteristics of long-term CWV variability are similar, whereas the main discrepancy among three datasets is located around the equatorial regions of the Intertropical Convergence Zone, the South Pacific Convergence Zone and warm cloud area, which is related with the difference between reanalysis models for the scheme of convective parameterization, the treatment of warm clouds, and the assimilation of satellite-based observations. Moreover, these CWV products are fairly consistent with observations (satellite-based retrievals) for oceans. On the other hand, there are systematic underestimations about 2.5 kg/m over lands for all three CWV datasets, compared with radiosonde observations. The difference between models to solve land-atmosphere interaction in complex environment, as well as the paucity in radiosonde observations, leads to significant water vapor gaps in the Amazon Basin of South America, central parts of Africa and some mountainous regions. These results would help better understand the climatology difference among various reanalysis datasets better, and more properly choose water vapor datasets for different research requirements. [ABSTRACT FROM AUTHOR]

Ground-based microwave radiometer profilers in the 20-60-GHz range operate continuously at numerous sites in different climate regions. Recent work suggests that a 1-D variational (1-DVAR) technique, coupling radiometric observations with outputs from a numerical weather prediction model, may outperform traditional retrieval methods for temperature and humidity profiling. The 1-DVAR technique is applied here to observations from a commercially available microwave radiometer deployed at Whistler, British Columbia, which was operated by Environment Canada to support nowcasting and short-term weather forecasting during the Vancouver 2010 Winter Olympic and Paralympic Winter Games. The analysis period included rain, sleet, and snow events (~235-mm total accumulation and rates up to 18 mm/h). The 1-DVAR method is applied “quasi-operationally,” i.e., as it could have been applied in real time, as no data were culled. The 1-DVAR-achieved accuracy has been evaluated by using simultaneous radiosonde and ceilometer observations as reference. For atmospheric profiling from the surface to 10 km, we obtain retrieval errors within 1.5 K for temperature and 0.5 g/m3 for water vapor density. The retrieval accuracy for column-integrated water vapor is 0.8 kg\m2, with small bias (-0.1 kg\m2) and excellent correlation (0.96). The retrieval of cloud properties shows a high probability of detection of cloud/no cloud (0.8/0.9, respectively), low false-alarm ratio (0.1), and cloud-base height estimate error within ~0.60 km. [ABSTRACT FROM AUTHOR]