Weather and climate forecasts: Difference between revisions
No edit summary |
|||
| Line 1: | Line 1: | ||
==Overview== | ==Overview== | ||
| Line 24: | Line 17: | ||
==Weather forecasts== | ==Weather forecasts== | ||
Weather forecasts have relatively high skill, and continue to improve. In the Western U.S., the typical forecast of daily high temperature (T_max) made 5 days in advance has a mean error of 3-4 | Weather forecasts have relatively high skill, and continue to improve. In the Western U.S., the typical forecast of daily high temperature (T_max) made 5 days in advance has a mean error of 3-4 °F, and the probability-of-precipitation (PoP) forecast at that same 5-day lead time is far more accurate than just assuming the average PoP for that season and location. In general, today's 5-day weather forecast is as skillful as the 4-day forecast was 10 years ago, or the 3-day forecast 20 years ago. | ||
The main source of predictability and skill for weather forecasts is the accurate description of the initial conditions of the atmosphere by global networks of observations from land and ocean stations, satellites, and aircraft. Once initialized with those observations, a weather forecast model simulates the evolution of the large-scale weather patterns. The skill of weather forecasts declines | The main source of predictability and skill for weather forecasts is the accurate description of the initial conditions of the atmosphere by global networks of observations from land and ocean stations, satellites, and aircraft. Once initialized with those observations, a weather forecast model simulates the evolution of the large-scale weather patterns. The skill of weather forecasts declines as the lead time lengthens, because the information contained in the initial patterns is gradually lost to unpredictable chaotic motions of the atmosphere. | ||
Weather forecast models are massively complex physics-based simulation models that are run on supercomputers. They have a basic structure, equations, and features akin to Global climate models (GCMs). Like GCMs, most weather models are global in scope, though very-high-resolution regional weather models are also used for short-term forecasts of 48 hours or less. For weather forecasts for the U.S., the NOAA National Weather Service relies mainly on the NOAA GFS (Global Forecast System) model, supplemented with the primary European and Canadian global forecast models, and higher-resolution U.S. regional models. Forecasters at the local NWS offices use the forecast model output as ''guidance'', often manually tuning the official forecasts in accordance with their knowledge of local weather and the strengths and weaknesses of the models. | Weather forecast models are massively complex physics-based simulation models that are run on supercomputers. They have a basic structure, equations, and features akin to Global climate models (GCMs). Like GCMs, most weather models are global in scope, though very-high-resolution regional weather models are also used for short-term forecasts of 48 hours or less. For weather forecasts for the U.S., the NOAA National Weather Service relies mainly on the NOAA GFS (Global Forecast System) model, supplemented with the primary European and Canadian global forecast models, and higher-resolution U.S. regional models. Forecasters at the local NWS offices use the forecast model output as ''guidance'', often manually tuning the official forecasts in accordance with their knowledge of local weather and the strengths and weaknesses of the models. | ||
| Line 33: | Line 26: | ||
==Subseasonal climate forecasts== | ==Subseasonal climate forecasts== | ||
Subseasonal climate forecasts, those made for conditions 2 weeks to 3 months ahead, are a relatively recent development leveraging advances in the understanding of climate-system processes at those timescales. Slotting into the gap in between weather forecasts and seasonal climate forecasts, subseasonal forecasts essentially blend the methods and sources of predictability used in both (Figure 1). Like seasonal climate forecasts, official NOAA subseasonal forecasts are expressed ''probabilistically'', as the likelihood of anomalous conditions. | |||
Subseasonal forecasts are based on the output of two overlapping classes of models: (1) weather forecast models, as described above, and (2) climate models, which have coarser temporal and spatial resolution but include more components, processes, and interactions, such as ocean currents and soil-atmosphere feedbacks. The climate models may be general-purpose GCMs, or purpose-built for subseasonal and seasonal climate forecasts. | |||
==Seasonal climate forecasts== | ==Seasonal climate forecasts== | ||
Revision as of 16:38, 25 May 2021
Overview
Weather and climate forecasts address the ubiquitous desire to know what the weather (temperature, precipitation, winds, cloud cover, etc.) will be like over the coming hours, days, weeks, and months. Weather forecasts refer to forecast periods of 14 days or less, a time frame over which the evolution of the current atmospheric pattern can be more directly predicted, and very specific and high-resolution (in time and space) predictions of weather variables can be made. The skill of weather forecasts over mid-latitude continental areas like the Colorado River Basin is very high for the first 1-2 days, lower for the next 3-4 days, and then drops off dramatically.
Climate forecasts or outlooks are for longer periods (14 days to several months or more), and over these time frames, what is typically forecasted is tendencies towards weather anomalies (warmer/cooler temperatures; higher/lower precipitation) rather than specific quantities. Both the spatial and temporal resolution of the climate forecasts tend to be lower. The sources of predictability for climate forecasts are more diverse than for weather forecasts, which depend mainly on capturing the initial conditions and patterns of the atmosphere. The skill of climate forecasts is much lower than for weather forecasts in all parts of the world, and this is true also in the Colorado River Basin.
Incomplete knowledge about future weather conditions is a major uncertainty in streamflow forecasts; most of the forecast error in April 1st seasonal streamflow forecasts is due to weather over the April-July period being different from that assumed in the most-probable forecast (the average of past April-July temperature and precipitation).

Relevance
Weather forecasts (out to 10 days) are routinely used by stakeholders across the basin for guiding short-term operational decision-making, such as in agriculture, recreation, and transportation, and water resource management. NOAA Colorado Basin River Forecast Center (CBRFC) incorporates weather forecasts (out to 10 days) into their short-term streamflow forecasts and peak runoff forecasts, and also into their Seasonal streamflow forecasts.
Climate forecasts and outlooks are consulted by stakeholders, including agricultural producers and water managers, but because of their relatively low forecasts accuracy/skill it is challenging to use them to support planning and decision-making like how weather forecasts and seasonal streamflow forecasts are used. Reclamation and CBRFC are exploring ways to incorporate climate forecasts into basin runoff and reservoir outlooks.
Weather forecasts
Weather forecasts have relatively high skill, and continue to improve. In the Western U.S., the typical forecast of daily high temperature (T_max) made 5 days in advance has a mean error of 3-4 °F, and the probability-of-precipitation (PoP) forecast at that same 5-day lead time is far more accurate than just assuming the average PoP for that season and location. In general, today's 5-day weather forecast is as skillful as the 4-day forecast was 10 years ago, or the 3-day forecast 20 years ago.
The main source of predictability and skill for weather forecasts is the accurate description of the initial conditions of the atmosphere by global networks of observations from land and ocean stations, satellites, and aircraft. Once initialized with those observations, a weather forecast model simulates the evolution of the large-scale weather patterns. The skill of weather forecasts declines as the lead time lengthens, because the information contained in the initial patterns is gradually lost to unpredictable chaotic motions of the atmosphere.
Weather forecast models are massively complex physics-based simulation models that are run on supercomputers. They have a basic structure, equations, and features akin to Global climate models (GCMs). Like GCMs, most weather models are global in scope, though very-high-resolution regional weather models are also used for short-term forecasts of 48 hours or less. For weather forecasts for the U.S., the NOAA National Weather Service relies mainly on the NOAA GFS (Global Forecast System) model, supplemented with the primary European and Canadian global forecast models, and higher-resolution U.S. regional models. Forecasters at the local NWS offices use the forecast model output as guidance, often manually tuning the official forecasts in accordance with their knowledge of local weather and the strengths and weaknesses of the models.
The primary forecast challenge for the Colorado River Basin pertaining to water resources is predicting the track and strength of the low-pressure systems (i.e., mid-latitude cyclones) coming off the Pacific that provide the vast majority of October-May precipitation. Once these systems reach the West Coast (usually 1-3 days before affecting the basin), they can be better monitored and forecasted.
Subseasonal climate forecasts
Subseasonal climate forecasts, those made for conditions 2 weeks to 3 months ahead, are a relatively recent development leveraging advances in the understanding of climate-system processes at those timescales. Slotting into the gap in between weather forecasts and seasonal climate forecasts, subseasonal forecasts essentially blend the methods and sources of predictability used in both (Figure 1). Like seasonal climate forecasts, official NOAA subseasonal forecasts are expressed probabilistically, as the likelihood of anomalous conditions.
Subseasonal forecasts are based on the output of two overlapping classes of models: (1) weather forecast models, as described above, and (2) climate models, which have coarser temporal and spatial resolution but include more components, processes, and interactions, such as ocean currents and soil-atmosphere feedbacks. The climate models may be general-purpose GCMs, or purpose-built for subseasonal and seasonal climate forecasts.
Seasonal climate forecasts
Decadal climate forecasts
Data and Tools
Additional Resources
State of the Science Report
Chapter 8 of the State of the Science report covers many aspects of Streamflow Forecasting in greater detail; Section 8.4 is specific to mid-range forecasts (seasonal and longer). Chapter 6, on Hydrologic Modeling, has a detailed description of the CBRFC forecast model framework in Section 6.3, under National Weather Service Models.
Research Directions
New and Notable Research (2020-present)
Summary