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Sources of Error in NWP Forecasts. or All the Excuses You’ll Ever Need Fred Carr. COMAP Symposium 00-1 Monday, 13 December 1999. Introduction.

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sources of error in nwp forecasts

Sources of Error in NWP Forecasts

or

All the Excuses You’ll Ever Need

Fred Carr

COMAP Symposium 00-1

Monday, 13 December 1999

introduction
Introduction

NWP has become an indispensable tool for the forecaster, but it is important to understand its limitations. There are many sources of possible error in an NWP forecast. If you keep these sources in mind as you examine NWP products, you should be able to make more intelligent use of the products in your forecasts.

These sources of error can be grouped into three categories:

A. Errors in the Initial Conditions

It is a complicated process to collect data from observations and get them into a form that an NWP model can use. Errors can occur at several steps along the way, as well as grow out of limitations of the data sources themselves.

intrinsic predictability limitations
Errors in the Initial Conditions

Observational Data Coverage

Spatial Density

Temporal Frequency

Errors in the Data

Instrument Errors

Representativeness Errors

Errors in Quality Control

Errors in Objective Analysis

Errors in Data Assimilation

Missing Variables

Errors in the Models

Equations of Motion Incomplete

Errors in Numerical Approximations

Horizontal Resolution

Vertical Resolution

Time Integration Procedure

Boundary Conditions

Horizontal

Vertical

Terrain

Physical Processes

Precipitation

Stratiform Precipitation

ii Convective Precipitation

Radiation

Surface Energy Balance

Boundary Layer

Surface Layer

ii Ekman or Mixed Layer

Intrinsic Predictability Limitations

Intrinsic Predictability Limitations
introduction4
Introduction

B. Errors in the Model

A model is by definition an approximation of reality, and although NWP models continue to grow in complexity, they cannot take into account all factors that affect the weather. The numerical solution of these models by computers introduces additional error.

C. Intrinsic Predictability Limitations

Even with error-free observations and a “perfect” model, forecast error will grow with time. There is an intrinsic limit to the range of a useful forecast. This range is short for small-scale phenomena and increases for synoptic and planetary-scale features.

intrinsic predictability limitations5
Errors in the Initial Conditions

Observational Data Coverage

Spatial Density

Temporal Frequency

Errors in the Data

Instrument Errors

Representativeness Errors

Errors in Quality Control

Errors in Objective Analysis

Errors in Data Assimilation

Missing Variables

Errors in the Models

Equations of Motion Incomplete

Errors in Numerical Approximations

Horizontal Resolution

Vertical Resolution

Time Integration Procedure

Boundary Conditions

Horizontal

Vertical

Terrain

Physical Processes

Precipitation

Stratiform Precipitation

ii Convective Precipitation

Radiation

Surface Energy Balance

Boundary Layer

Surface Layer

ii Ekman or Mixed Layer

Intrinsic Predictability Limitations

Intrinsic Predictability Limitations
intrinsic predictability limitations6
Intrinsic Predictability Limitations
  • Even with error-free observations and a “perfect” model, forecast errors will grow with time.
  • No matter what resolution of observations is used, there are always unmeasured scales of motion. The energy in these scales transfers both up and down scale. The upward transfer of energy from scales less than the observing resolution represents an energy source for larger-scale motions in the atmosphere that will not be present in the numerical model. Thus, the real atmosphere and the atmosphere that is represented in the numerical model are different. For this reason, the model forecast and the real atmosphere will diverge with time. This error growth is roughly equal to a doubling of error every 2-3 days. Therefore, even very small initial errors can result in major errors for a long-range forecast.
  • The problem just stated is the essence of chaos theory applied to meteorology. This theory proposes that nothing is entirely predictable, that even very small perturbations in a system result in unpredictable changes in time.
intrinsic predictability limitations7
This graphic illustrates the effect of intrinsic predictability limitations on forecast skill.

Forecasts based on climatology will have a relatively high level of error, but will remain constant over time. Forecasts based on persistence (i.e., whatever is happening now will happen later) are nearly perfect at extremely short range, but quickly deteriorate. Current models do well at short ranges, but eventually do worse than climatology. A forecast that is worse than climatology is considered useless.

Intrinsic Predictability Limitations
intrinsic predictability limitations8
Even the best model we can envision will, for reasons just discussed, produce forecasts that deteriorate over time to a quality lower than those based on climatology.

Our current forecast models have skill up to the 5-7 day range on the synoptic scale for 500 hPa heights. (Occasionally they have skill at 15-30 days for time-averaged planetary waves.) They show much less skill for derived quantities such as vorticity advection or precipitation.

Intrinsic Predictability Limitations
intrinsic predictability limitations9
Intrinsic Predictability Limitations
  • A related predictability limitation is that intrinsic error growth will contaminate smaller scales faster than larger scales. In other words, a small-scale phenomenon will be less well forecast than a large-scale phenomenon in the same range forecast.
  • However, mesoscale/convective scale predictability may not follow this smooth progression due to its highly intermittent nature. For example, a rotating supercell thunderstorm may have more predictability (2-6 hr) than an airmass thunderstorm (1 hr). Topographically and/or diurnally-forced circulations such as drylines and sea breezes are more predictable than squall lines.
concluding comment
Concluding Comment
  • It used to be thought that the errors due to horizontal resolution constituted about 30% of the total forecast error. However, due to faster computers (which have allowed more accurate numerical schemes and higher resolution) this is no longer the case.
  • Currently, the largest source of error is more likely to be the unavailability of high resolution data over the entire forecast domain. One might now say that new (and accurate) observing systems, which measure the variables we need under all weather conditions, are the best way to improve NWP forecasts. Improvements in computers (which may allow higher horizontal and vertical resolution) and in the parameterizations of physical processes within the models will help, but to a lesser degree than new observing systems.