100 Years of Progress in Mesoscale Planetary Boundary Layer Meteorological Research

This chapter outlines the development of our understanding of several examples of mesoscale atmospheric circulations that are tied directly to surface forcings, starting from thermally driven variations over the ocean and progressing inland to man-made variations in temperature and roughness, and ending with forced boundary layer circulations. Examples include atmospheric responses to 1) overocean temperature variations, 2) coastlines (sea breezes), 3) mesoscale regions of inland water (lake-effect storms), and 4) variations in land-based surface usage (urban land cover). This chapter provides brief summaries of the historical evolution of, and tools for, understanding such mesoscale atmospheric circulations and their importance to the field, as well as physical processes responsible for initiating and determining their evolution. Some avenues of future research we see as critical are provided. The American Meteorological Society (AMS) has played a direct and important role in fostering the development of understanding mesoscale surface-forced circulations. The significance of AMS journal publications and conferences on this and interrelated atmospheric, oceanic, and hydrological fields, as well as those by sister scientific organizations, are demonstrated through extensive relevant citations.

Multivariate Time Series Similarity Searching

Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factorSPCA, and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches.

Online multistep-ahead inundation depth forecasts by recurrent NARX networks

Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields, but relatively scant on multistep-ahead flood inundation forecasting, which is very difficult to achieve, especially when dealing with forecasts without regular observed data. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model at thirteen inundation-prone sites in Yilan County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit error growth and accumulation when being applied to online multistep-ahead inundation forecasts over a long lasting forecast period. For comparison, a feedforward time-delay and an online feedback configuration of NARX networks (T-NARX and O-NARX) were performed. The results show that (1) T-NARX networks cannot make online forecasts due to unavailable inputs in the constructed networks even though they provide the best performances for reference only; and (2) R-NARX networks consistently outperform O-NARX networks and can be adequately applied to online multistep-ahead forecasts of inundation depths in the study area during typhoon events.

On-line multistep-ahead inundation depth forecasts by recurrent NARX networks

Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields, but relatively scant on flood inundation forecast. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model at thirteen inundation-prone sites in Yilan County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit error growth and accumulation when being applied to on-line multistep-ahead inundation forecasts over a long lasting forecast period. For comparison, a feedforward time-delay and an on-line feedback configuration of NARX networks (T-NARX and O-NARX) were performed. The results show that (1) T-NARX networks cannot make on-line forecasts due to unavailable inputs in the constructed networks even though they provide the best performances for reference only; and (2) R-NARX networks consistently outperform O-NARX networks and can be adequately applied to on-line multistep-ahead forecasts of inundation depths in the study area during typhoon events.

Assessment and Enhancement of MERRA Land Surface Hydrology Estimates

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979–present. This study introduces a supplemental and improved set of land surface hydrological fields (“MERRA-Land”) generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF Re-Analysis-Interim (ERA-I). MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 U.S. stations) are comparable and significantly greater than that of MERRA. Throughout the Northern Hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 U.S. basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies.

Developing an independent, generic, phosphorus modelling component for use with grid-oriented, physically based distributed catchment models

Grid-oriented, physically based catchment models calculate fields of various hydrological variables relevant to phosphorus detachment and transport. These include (i) for surface transport: overland flow depth and flow in the coordinate directions, sediment load, and sediment concentration and (ii) for subsurface transport: soil moisture and hydraulic head at various depths in the soil. These variables can be considered as decoupled from any chemical phosphorus model since phosphorus concentrations, either as dissolved or particulate, do not influence the model calculations of the hydrological fields. Thus the phosphorus concentration calculations can be carried out independently from and after the hydrological calculations. This makes it possible to produce a separate phosphorus modelling component which takes as input the hydrological fields produced by the catchment model and which calculates, at each simulation time step, the phosphorus concentrations in the flows. This paper summarises the equations and structure of such a Grid Oriented Phosphorus Component (GOPC) developed by the authors for simulating phosphorus concentrations and loads using the outputs of a fully distributed physical based hydrological model. The GOPC performance is illustrated by an example of a simplified hypothetical catchment subjected to some ideal conditions.