| NATS 101 Lecture 18 Weather Forecasting |
| Review: ET Cyclones Ingredients for Intensification |
| Strong Temperature Contrast | |
| Jet Stream Overhead | |
| S/W Trough to West | |
| UL Divergence over Surface Low | |
| If UL Divergence exceeds LL Inflow, Cyclone Deepens | |
| Similar Life Cycles |
| Reasons to Forecast Weather & Climate |
| Should I bring my umbrella to work today? | |
| Should Miami be evacuated for a hurricane? | |
| How much heating oil should a refinery process for the upcoming winter? | |
| Will the average temperature change if CO2 levels double during the next 100 years? | |
| How much to charge for flood insurance? | |
| How much water will be available for agriculture & population in 30 years | |
| These questions require weather-climate forecasts for today, a few days, months, years, decades |
| Forecasting Questions |
| How are weather forecasts made? | |
| How accurate are current weather forecasts? | |
| How accurate can weather forecasts be? | |
| Types of Forecasts |
| Persistence - forecast the future atmospheric state to be the same as current state | |
| -Raining today, so forecast rain tomorrow | |
| -Useful for few hours to couple days |
| Types of Forecasts |
| Trend - add past change to current condition to obtain forecast for the future state | |
| -Useful for few hours to couple days |
| Types of Forecasts |
| Analog - find past state that is most similar to current state, then forecast same evolution | |
| -Difficulty is that no two states exactly alike | |
| -Useful for forecasts up to one or two days |
| Types of Forecasts |
| Climatology - forecast future state to be same as climatology or average of past weather for date | |
| -Forecast July 4th MAX for Tucson to be 100 F | |
| -Most accurate for long forecast projections, forecasts longer that 30 days |
| Types of Forecasts |
| Numerical Weather Prediction (NWP) - use mathematical models of physics principles to forecast future state from current conditions. | ||
| Process involves three major phases | ||
| Analysis Phase (estimate present conditions) | ||
| Prediction Phase (computer modeling) | ||
| Post-Processing Phase (use of products) | ||
| To justify NWP cost, it must beat forecasts of persistence, trend, analog and climatology | ||
| Analysis Phase |
| Purpose: Estimate the current weather conditions to use to initialize the weather forecast | |
| Implementation: Because observations are always incomplete, the Analysis is accomplished by combining observations and the most recent forecast |
| Analysis Phase |
| Current weather conditions are observed around the global (surface data, radar, weather balloons, satellites, aircraft). | |
| Millions of observations are transmitted via the Global Telecommunication System (GTS) to the various weather centers. | |
| U.S. center is in D.C. and is named National Centers for Environmental Prediction (NCEP) |
| Analysis Phase |
| The operational weather centers sort, archive, and quality control the observations. | |
| Computers then analyze the data and draw maps to help us interpret weather patterns. | |
| Procedure is called Objective Analysis. | |
| Final chart is referred to as an Analysis. | |
| Computer models at weather centers make global or national weather forecast maps |
| Surface Data |
| Surface Buoy Reports |
| Radiosonde Coverage |
| Aircraft Reports |
| Weather Satellites |
| Satellite observations fill data void regions | |
| Geostationary Satellites | |
| High temporal sampling | |
| Low spatial resolution | |
| Polar Orbiting Satellites | |
| Low temporal sampling | |
| High spatial resolution |
| Obs from Geostationary Satellites |
| Temperature from Polar Satellites |
| Operational ECMWF system September to December 2008. Averaged over all model layers and entire global atmosphere. % contribution of different observations to reduction in forecast error. |
| Atmospheric Models |
| Weather models are based on mathematical equations that retain the most important aspects of atmospheric behavior | |
| - Newton's 2nd Law (density, press, wind) | |
| - Conservation of mass (density, wind) | |
| - Conservation of energy (temp, wind) | |
| - Equation of state (density, press, temp) | |
| Governing equations relate time changes of fields to spatial distributions of the fields | |
| e.g. warm to south + southerly winds Þ warming |
| Prediction Phase |
| Analysis of the current atmospheric state (wind, temp, press, moisture) are used to start the model equations running forward in time | |
| Equations are solved for a short time period (~5 minutes) over a large number (107 to 108) of discrete locations called grid points | |
| Grid spacing is 2 km to 50 km horizontally and 100 m to 500 m vertically |
| Model Grid Boxes |
| ÒA Lot Happens Inside a
Grid BoxÓ (Tom Hamill, CDC/NOAA) |
| Approximate Size of One Grid Box for NCEP Global Ensemble Model | |
| Note Variability in Elevation, Ground Cover, Land Use |
| 13 km Model Terrain |
| Post-Processing Phase |
| Computer draws maps of projected state to help humans interpret weather forecast | |
| Observations, analyses and forecasts are disseminated to private and public agencies, such as the local NWS Forecast Office and UA | |
| Forecasters use the computer maps, along with knowledge of local weather phenomena and model performance to issue regional forecasts | |
| News media broadcast these forecasts to public |
| Suite of Official NWS Forecasts |
| Summary: Key Concepts |
| Forecasts are needed by many users | |
| There are several types of forecasts | |
| Numerical Weather Prediction (NWP) | |
| Use computer models to forecast weather | |
| -Analysis Phase | |
| -Prediction Phase | |
| -Post-Processing Phase | |
| Humans modify computer forecasts |
| Summary: Key Concepts |
| National Centers for Environment Prediction (NCEP) issues operational forecasts for | |
| El Nino tropical SST anomalies | |
| Seasonal outlooks | |
| 10 to 15 day weather forecasts | |
| 2 to 3 day fine scale forecasts |
| NATS 101 Weather Forecasting 2 |
| 3-Month SST Forecast (Issued 6 April 2004) |
| SST forecasts for the El Nino region of tropical Pacific are a crucial component of seasonal and yearly forecasts. | |
| Forecasts of El Nino and La Nina show skill out to around 12 months. | |
| 1997-98 El Nino forecast was somewhat accurate once the El Nino was established |
| Winter 2004-2005
Outlook (Issued 20 October 2005) |
| Winter 2004-2005
Outlook (Issued 18 March 2004) |
| Winter 2004-2005
Outlook (Issued 18 March 2004) |
| NCEP GFS Forecasts |
| ATMO GFS Link | |
| NCEP global forecast; 4 times per day | |
| Run on 50 km grid (approximately) | |
| GFS gives the best 2-10 day forecasts | |
| NCEP GFS Forecasts |
| ATMO NAM Link | |
| NCEP CONUS forecast; 4 times per day | |
| Run on 12 km grid (approximately) | |
| NAM gives the best 24 h precip forecasts | |
| Different Forecast Models |
| Different, but equally defensible models produce different forecast evolutions for the same event. | |
| Although details of the evolutions differ, the large-waves usually evolve very similarly out to 2 days. |
| Forecast Evaluation: Accuracy and Skill |
| Accuracy measures the closeness of a forecast value to a verifying observation | |
| Accuracy can be measured by many metrics | |
| Skill compares the accuracy of a forecast against the accuracy of a competing forecast | |
| A forecast must beat simple competitors: | |
| Persistence, Climatology, Random, etc. | |
| If forecasts consistently beat these competitors, then the forecasts are said to be ÒskillfulÓ |
| How Humans Improve Forecasts |
| Local geography in models is smoothed out. | |
| Model forecasts contain small, regional biases. | |
| Model surface temperatures must be adjusted, and local rainfall probabilities must be forecast based on experience and statistical models. | |
| Small-scale features, such as thunderstorms, must be inferred from long-time experience. | |
| If model forecast appears systematically off, human corrects it using current information. |
| Humans Improve Model Forecasts |
| Forecasters perform better than automated model and statistical forecasts for 24 and 48 h. | |
| Human forecasters play an important role in the forecasting process, especially during severe weather situations that impact public safety. |
| Current Skill |
| 0-12 hrs: Can track individual severe storms | |
| 12-48 hrs: Can predict daily weather changes well, including regions threatened by severe weather. | |
| 3-5 days: Can predict major winter storms, excessive heat and cold snaps. Rainfall forecasts are less accurate. | |
| 6-15 days: Can predict average temp and rain over 5 day period well, but daily changes are not forecast well. | |
| 30-90 days: Slight skill for average temp and rainfall over period. Forecasts use combination of model forecasts and statistical relationships (e.g. El Nino). | |
| 90-360 days: ÒSlightÓ skill for SST anomalies. |
| Why NWP Forecasts Go Awry |
| There are inherent flaws in all NWP models that limit the accuracy and skill of forecasts | |
| Computer models idealize the atmosphere | |
| Assumptions can be on target for some situations and way off target for others |
| Why NWP Forecasts Go Awry |
| All analyses contain errors | |
| Regions with sparse or low quality observations | |
| - Oceans have ÒpoorerÓ data than continents | |
| Instruments contain measurement error | |
| - A 20oC reading does not exactly equal 20oC | |
| Even a precise measurement at a point location might not accurately represent the big picture | |
| - Radiosonde ascent through isolated cumulus |
| Why NWP Forecasts Go Awry |
| Insufficient resolution | |
| Weather features smaller than the grid point spacing do not exist in computer forecasts | |
| Interactions between the resolved larger scales and the excluded smaller scales are absent | |
| Inadequate representations of physical processes such as friction and heating | |
| Energy and moisture transfer at the earth's surface are not precisely known |
| Chaos: Limits to Forecasting |
| We now know that even if our models were perfect, it would still be impossible to predict precisely winter storms beyond 10-14 days | |
| There are countless, undetected small errors in our initial analyses of the atmosphere | |
| These small disturbances grow with time as the computer projects farther into the future | |
| Lorenz posed, ÒDoes the flap of a butterflyÕs wings in Brazil set off a tornado in Texas?Ó |
| Chaos: Limits to Forecasting |
| After a few days, these initial imperfections dominate forecasts, rendering it useless. | |
| Chaotic physical systems are characterized by unpredictable behavior due to their sensitivity to small changes in initial state. | |
| Evolutions of chaotic systems in nature might appear random, but they are bounded. | |
| Although bounded, they are unpredictable. |
| Chaos: Kleenex Example |
| Drop a Kleenex to the floor | |
| Drop a 2nd Kleenex, releasing it from the same spot | |
| Drop a 3rd Kleenex, releasing it from the same spot, etc. | |
| Repeat procedureÉ1,000,000 times if you like, even try moving closer to the floor | |
| Does a Kleenex ever land in the same place as a prior drop? | |
| Kleenex exhibits chaotic behavior! |
| Atmospheric Predictability |
| The atmosphere is like a falling Kleenex! | |
| The uncertainty in the initial conditions grow during the evolution of a weather forecast. | |
| So a point forecast made for a long time will ultimately be worthless, no better than a guess! | |
| There is a limited amount of predictability, but only for a short period of time. | |
| Loss of predictability is an attribute of nature. It is not an artifact of computer models. |
| Limits of Predictability |
| What determines the limits of predictability for the atmosphere? | |
| Limits dependent on many factors such as: | |
| Flow regime | |
| Geographic location | |
| Spatial scale of disturbance | |
| Weather element |
| Sensitivity to Initial Conditions |
| Summary: Key Concepts |
| NCEP issues forecasts out to a season. | |
| Human forecasters improve NWP forecasts. | |
| NWP forecast go awry for several reasons: | |
| measurement and analysis errors | |
| insufficient model resolution | |
| incomplete understanding of physics | |
| chaotic behavior and predictability | |
| Chaos always limits forecast skill. |
| Assignment for Next Lecture |
| Topic - Weather Forecasting Part II | |
| Reading - Ahrens pg 249-260 | |
| Problems - 9.11, 9.15, 9.18 | |
| Topic - Thunderstorms | |
| Reading - Ahrens pg 263-276 | |
| Problems – | |
| 10.1, 10.3, 10.4, 10.5, 10.6, 10.7, 10.16 |