SBIR Phase I: Real-time Objective Model Analysis Tool and Multi-Model Ensemble Forecast System

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是通过提供技术创新产品来帮助业务预报员创建更好的天气预报，从而推进社会目标，如保护生命和财产，防止收入损失和改进规划。高性能计算集群可用性的爆炸性增长大大增加了业务气象学家可用的预测数据量。及时理解所有可用的数据已经变得很困难，市场上存在简化这一过程的产品。天气预报的任何改进都将对企业和社会产生直接和间接的积极影响，通过提高安全性，帮助日常和每周计划，帮助减少与风暴相关的费用，帮助企业避免与天气相关的费用，然后为客户节省成本。该技术将使预报员能够更快地做出更好的预报，并将在国家气象中心的操作环境中，对个人用户以及能源，农业和媒体等许多商业应用中发挥重要作用。这个小型企业创新研究（SBIR）第一阶段项目旨在展示实时合成全球、区域和综合天气模型预报数据的多个流的技术可行性，以提供模型性能的简化产品，然后使用该信息创建新的全球或区域预报地图。访问和分析大量预测数据的复杂性给预报员的业务带来了重大挑战。分析所有可用的预报数据是困难的，因为预报员面临着在短时间内处理数据的压力。此外，这种分析通常是主观的。提出的技术将通过创建一个模型性能和预测产品来改善这些问题，该产品将大部分预测数据整合到一个易于使用的界面中。该项目的目标是为可操作的预报员提供一种工具，该工具可以以最小的努力客观地观察模型的性能，并为他们提供比任何单一模型更好的新预测产品。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to advance societal goals such as protection of life and property, protection against lost revenue and improved planning by providing a technically innovative product to help operational forecasters create better weather forecasts. The explosive growth in the availability of high-performance computing clusters has significantly increased the amount of forecast data available to operational meteorologists. Making sense of all of the available data in a timely fashion has become difficult and the market exists for products that streamline this process. Any improvement in weather forecasting will have direct and indirect positive effects for businesses and society by increasing safety, helping in daily and weekly planning, helping to mitigate storm related expenses and helping businesses avoid weather-related expenses that can then trickle down as cost-savings for the customer. The technology will allow forecasters to produce better forecasts more quickly and will be valuable in operational settings at national weather centers, to individual users, and in a host of commercial applications such as energy, agriculture, and media.This Small Business Innovation Research (SBIR) Phase I project seeks to demonstrate the technical feasibility of operationally synthesizing multiple streams of global, regional, and ensemble weather model forecast data in real-time to provide a streamlined product of model performance and then use that information to create new global or regional forecast maps. The complexity of accessing and analyzing vast amounts of forecast data creates a significant challenge to the operational forecaster. Analysis of all available forecast data is difficult since forecasters are under pressure to process the data in a short amount of time. Furthermore, the analysis is usually subjective. The proposed technology will ameliorate these issues by creating a model performance and forecast product that consolidates a majority of forecast data into one, easy-to-use interface. The goal of the project is to provide operational forecasters with a tool that gives an objective look at model performance with minimal effort and provides them with a new forecast product that is better than any single model.