CyberSEES:Type 2: Precipitation Estimation from Multi-Source Information using Advanced Machine Learning

该项目将开发一个由网络支持的、数据驱动的建模系统，该系统将使用大量的地球和环境观测数据来估计降雨量，目的是进一步促进淡水资源的可持续性。这项研究的主要目标之一是创新性地应用先进的机器学习技术来预测复杂的自然现象。还将探讨当前机器学习算法在应用于准确估计降雨量所需的地球和环境数据时的代表性和计算限制。具体地说，该项目将探索基于图像的特征提取技术和所谓的“深度信念”模型，用于解释天气和气候数据中的重要特征，以估计和预测降雨量。待调查的特征包括外观、纹理、形状、动态以及区域天气和气候特征。该项目将使用来自地面雷达、卫星传感器和数值天气预报(NWP)模型的数据，以及来自陆地表面数据集的物理特征。基于图像的提取技术将产生特征地图，这些特征地图将被用作深度玻尔兹曼机(DBM)模型的输入，DBM模型是神经网络的现代版本。DBM表示可见单元和隐藏单元集合上的概率模型，并生成目标变量(在本例中为降雨量)作为输出。该模型特别适用于所提出的建模方法和大规模数据。一旦这个系统被开发出来，我们将利用各种各样的地球科学数据来提供准确、高分辨率的全球降水量估计。用于评估降水量估计的验证方法将包括大气和水文科学使用的一般统计、降水强度分布和区域分析方法。该项目将专注于在美国上空进行核查，那里可以获得高分辨率的地面雷达数据。近几十年来，极端洪涝和干旱变得更加频繁和严重。降水模式的变化归因于气候的多变性，是造成这些极端水文现象的原因，并造成淡水资源管理和规划方面的不确定性。面对地球上不断增长的人口和水资源的压力，重要的是将这些不确定性和这些自然灾害的社会影响降到最低。只有通过准确的降水测量和预报才能实现这些目标。卫星平台和先进的数值模式产生了大量可用于这一目的的全球高时间和空间分辨率数据，但分析这些数据仍然是一个挑战。最近在计算科学和机器学习方面的创新扩大了我们从遥感数据中获取关键信息的能力，这些信息对于理解云降水系统至关重要。该项目将通过使用计算科学、统计建模技术(机器学习)、遥感观测和数值模型来调整和改进基于卫星的降水估计算法，以开发能够有效分析海量观测数据的网络建模系统，以提高高空间和时间分辨率的全球降水估计。

This project will develop a cyber-enabled, data-driven modeling system that will use the vast amount of earth and environmental observational data to estimate precipitation, with the goal to further freshwater resource sustainability. One of the major objectives of this research is to innovatively apply advanced machine learning techniques to predict complex natural phenomena. Identification of current machine learning algorithms' representational and computational limitations when applied to earth and environmental data that are required for accurately estimating precipitation will also be explored. Specifically, this project will explore image-based feature extraction techniques and so-called "deep belief" models for interpreting important features within weather and climate data to estimate and predict precipitation. Features to be investigated include appearance, texture, shape, dynamics, and regional weather and climate signatures. The project will use data from ground-based radar, satellite-based sensors, and Numerical Weather Prediction (NWP) models, as well as physical characteristics from land surface datasets. Image-based extraction techniques will produce feature maps, which in turn will be used as input into a Deep Boltzmann Machine (DBM) model, a modern version of neural networks. A DBM represents a probability model over a collection of visible units and hidden units and produces as output a target variable (in this case, precipitation). This model is particularly suitable for the proposed modeling approach and for large-scale data. Once this system is developed, we will utilize a wide variety of earth science data to provide accurate, high-resolution global precipitation estimates. Verification methods to be used for evaluating precipitation estimates will include general statistics, precipitation intensity distribution, and regional analysis methods used by the atmospheric and hydrological sciences. The project will focus on verification over the United States, where high-resolution ground-based radar data are available. In recent decades, extreme flooding and droughts have become more frequent and severe. Changing patterns in precipitation, which are attributed to climate variability, are responsible for these hydrologic extremes and contribute to uncertainties in freshwater resource management and planning. In the face of the planet's growing population and stresses on water resources, it is important to minimize these uncertainties and the social impacts of these natural hazards. These goals can only be accomplished through accurate precipitation measurements and forecasts. Satellite platforms and advanced numerical models produce massive amounts of global high temporal and spatial resolution data that can be used for this purpose, but analyzing these data remains a challenge. Recent innovations in computational sciences and machine learning have extended our capability to harvest from remotely-sensed data critical information that is essential to understanding cloud-precipitation systems. This project will adapt and improve satellite-based precipitation estimation algorithms by using computational science, statistical modeling techniques (machine learning), remote-sensing observations, and numerical models to develop cyber-enabled modeling systems that can effectively analyze the massive amount of observational data to improve the global estimation of precipitation at high spatial and temporal resolutions.