Intercomparison of cloud model simulations of Arctic mixed‐phase boundary layer clouds observed during SHEBA/FIRE‐ACE

An intercomparison of six cloud‐resolving and large‐eddy simulation models is presented. This case study is based on observations of a persistent mixed‐phase boundary layer cloud gathered on 7 May, 1998 from the Surface Heat Budget of Arctic Ocean (SHEBA) and First ISCCP Regional Experiment ‐ Arctic Cloud Experiment (FIRE‐ACE). Ice nucleation is constrained in the simulations in a way that holds the ice crystal concentration approximately fixed, with two sets of sensitivity runs in addition to the baseline simulations utilizing different specified ice nucleus (IN) concentrations. All of the baseline and sensitivity simulations group into two distinct quasi‐steady states associated with either persistent mixed‐phase clouds or all‐ice clouds after the first few hours of integration, implying the existence of multiple states for this case. These two states are associated with distinctly different microphysical, thermodynamic, and radiative characteristics. Most but not all of the models produce a persistent mixed‐phase cloud qualitatively similar to observations using the baseline IN/crystal concentration, while small increases in the IN/crystal concentration generally lead to rapid glaciation and conversion to the all‐ice state. Budget analysis indicates that larger ice deposition rates associated with increased IN/crystal concentrations have a limited direct impact on dissipation of liquid in these simulations. However, the impact of increased ice deposition is greatly enhanced by several interaction pathways that lead to an increased surface precipitation flux, weaker cloud top radiative cooling and cloud dynamics, and reduced vertical mixing, promoting rapid glaciation of the mixed‐phase cloud for deposition rates in the cloud layer greater than about 1 − 2 × 10−5 g kg−1 s−1 for this case. These results indicate the critical importance of precipitation‐radiative‐dynamical interactions in simulating cloud phase, which have been neglected in previous fixed‐dynamical parcel studies of the cloud phase parameter space. Large sensitivity to the IN/crystal concentration also suggests the need for improved understanding of ice nucleation and its parameterization in models

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

The increasing frequency of devastating floods from heavy rainfall—associated with climate change—has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep-learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is a long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with RMSE less than 67 cm for about 10 m of stage peaks, as well as accurately simulating stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision, we found that the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well-simulated, the physical concepts of runoff appear to be soundly embedded in the MLP

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

The increasing frequency of devastating floods from heavy rainfall—associated with climate change—has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep-learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is a long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with RMSE less than 67 cm for about 10 m of stage peaks, as well as accurately simulating stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision, we found that the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well-simulated, the physical concepts of runoff appear to be soundly embedded in the MLP