Reducing bias in OAR’s Earth System Model is an indicator of the improved skill of precipitation simulation. Bias is defined as the long-term mean (or average over time) difference between a simulated (model generated) value and the observed value. This performance measure will track the reduction in bias (e.g., percent reduction of the current FY21 bias) in U.S. seasonal precipitation simulations in OAR Earth System models based on a percentage of bias in past Earth System models.

Proposed new indicator for FY 2022. Measure is under development.

The Arctic is a vast, remote region that is critical to the United States economy, weather and sea ice forecasting, our understanding of ecosystem health (including marine mammals and fisheries), and our understanding of climate change. Due to the presence of low temperatures, harsh sea conditions, limited telecommunications infrastructure, and sea ice, maintaining a network of sea ice buoys, Air-Launched Autonomous Micro-Observers, and Bering & Chukchi moorings is difficult, necessitating constant deployment of a combination of expendable technologies and retrieval and redeployment of a large network of moorings in the Bering and Chukchi Seas.

This KPI looks into the Patents service level on customers that indicated the patents employees they interacted with were helpful. This is in line with OMB A-11 Sec. 280 HISP requirements.

Proposed new indicator for FY 2022. Measure is under development.

Proposed new indicator for FY 2022. Measure is under development.

Improve the accuracy and specificity of extreme rainfall forecasts as measured by the Threat Score of the 2” threshold, made 3 days in advance. Given this score is sensitive to the type of rainfall events experienced, and varies by year, we are adopting a 3 year running average. For FY22 and FY23, we are targeting roughly a 25% overlap between the forecast and observed area (0.14). For comparison, a Threat Score of 0 represents no overlap between forecast & observed location and a Threat Score of 1 represents complete overlap between forecast & observed location. The formula for calculating the Threat Score is = Overlap / (Forecast + Observed - Overlap).

Improve the accuracy and specificity of extreme rainfall forecasts as measured by the Threat Score of the 2” threshold, made 3 days in advance. Given this score is sensitive to the type of rainfall events experienced, and varies by year, we are adopting a 3 year running average. For FY22 and FY23, we are targeting roughly a 25% overlap between the forecast and observed area (0.14). For comparison, a Threat Score of 0 represents no overlap between forecast & observed location and a Threat Score of 1 represents complete overlap between forecast & observed location. The formula for calculating the Threat Score is = Overlap / (Forecast + Observed - Overlap).

Reducing bias in OAR’s Earth System Model is an indicator of the improved skill of precipitation simulation. Bias is defined as the long-term mean (or average over time) difference between a simulated (model generated) value and the observed value. This performance measure will track the reduction in bias (e.g., percent reduction of the current FY21 bias) in U.S. seasonal precipitation simulations in OAR Earth System models based on a percentage of bias in past Earth System models.

The Argo program is responsible for providing real-time data for the upper 2000m of ocean heat content globally and for important regions like the Tropical Pacific for detection of El Nino.