Observation errors in all‐sky data assimilation

Geer, Alan; Bauer, Péter

doi:10.1002/qj.830

Cited by 165 publications

(223 citation statements)

References 44 publications

Supporting

Mentioning

201

Contrasting

Unclassified

Order By: Relevance

“…The demonstration that such diagnostics can have a real beneficial impact on the development of an operational forecast system is encouraging. More reliable initialization of ensemble forecasts can also come from improvements in the flow-specific modeling of observation error characteristics and "observation operators," which map forecast model fields to observed quantities (Geer and Bauer 2011). Again, such modeling developments should lead to improved forecast skill.…”

Section: Model or Observation Uncertainty?mentioning

confidence: 99%

Flow-Dependent Reliability: A Path to More Skillful Ensemble Forecasts

Rodwell

Richardson

Parsons

et al. 2018

Bulletin of the American Meteorological Society

View full text Add to dashboard Cite

N umerical weather prediction is fundamentally a probabilistic task owing to the growth of unavoidable uncertainties in the forecast's initial conditions and in the forecast model itself (Sutton 1954;Lorenz 1963). A key question for the user is how certain they can be that a "10% probability of precipitation" really means that they will be unlucky to get wet. How would they assess the reliability of such a prediction? One approach would be for them to keep a record of the days when the forecast indicated a 10% probability of precipitation and to see if rainfall actually occurred on 10% of those days. If, in reality, it occurred on 20% of the days, this would indicate that these probabilistic forecasts were unreliable. On the other hand if, for a large set of occasions when the forecast gave a 40% chance of a hurricane making landfall in a particular region, a hurricane did make landfall 40% of the time, this would represent a "reliable" (Sanders 1958) set of forecasts. If the decision about whether to defend a vulnerable piece of infrastructure could be based purely on the forecast, then, since the forecast is reliable, a simple approach would be to defend the infrastructure if the cost to do so was less than 40% of the loss that would otherwise occur (Richardson 2000). In reality other factors will influence such a decision, but this example does highlight the importance of reliability. We may ask how we could do even better. Suppose we could partition the forecasts into two categories (based on the initial flow conditions) where the probabilities of landfall were, say, 60% and 20% (i.e., one flow situation is more likely to lead to landfall than the other), and suppose that the forecasts in both categories were reliable. It is straightforward to show (see "The benefits of flow-dependent reliability" sidebar) that, under the assumptions of this simple decision model, the expected expense in defending the infrastructure is always reduced (or matched). This result, which does not depend on the choice of numbers, emphasizes the potential utility of improving flow-dependent reliability. The key question to address here is, How do we assess and improve the flow-dependent reliability of our forecasts? Before attempting to address this question, it Improving short-range flow-dependent reliability, which may provide a practical approach to increase forecast skill out to ~10 days, is discussed and illustrated for a specific flow situation associated with convection over North America and poor skill for Europe. 1015MAY 2018 AMERICAN METEOROLOGICAL SOCIETY |

show abstract

Section: Model or Observation Uncertainty?mentioning

confidence: 99%

Flow-Dependent Reliability: A Path to More Skillful Ensemble Forecasts

Rodwell

Richardson

Parsons

et al. 2018

Bulletin of the American Meteorological Society

View full text Add to dashboard Cite

show abstract

“…The bias correction scheme may not be proper for cloudy observations because of the usage of an asymmetric predictor (Geer and Bauer, 2011) that is the FG rain amount in the 1D+4D-Var system. Some biases are very large, and they may be due to errors in the structure and intensity of forecast cloud and rain, but may also be due to displacement errors.…”

Section: The Ecmwf 1d+4d-var Algorithmmentioning

confidence: 99%

Comparing rain retrievals from GPROF with ECMWF 1D‐Var products

Wang

Kummerow

Geer

et al. 2012

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

Both the Goddard Profiling Algorithm (GPROF) and European Centre for MediumRange Weather Forecasts (ECMWF) one-dimensional + four-dimensional variational analysis (1D+4D-Var) rainfall retrievals are inversion algorithms based on Bayes' theorem. Differences stem primarily from the a priori information. The GPROF uses an observationally generated a priori database whereas ECMWF 1D-Var uses the model forecast first guess (FG) fields. The relative similarity in the two approaches means that comparisons can shed light on the differences that are produced by the a priori information. Case studies have found that differences can be classified into four categories based upon the agreement in the brightness temperatures (T b s) and in the microphysical properties of cloud water path (CWP) and rainwater path (RWP) space. A category of special interest is when both retrievals converge to similar T b through minimization procedures but produce different CWP and RWP. The similarity in T b can be attributed to comparable total water path (TWP) between the two retrievals while the disagreement in the microphysics is caused by their different degrees of constraint of the cloud/rain ratio by the observations. This situation occurs frequently and takes up 46.9% in the 1 month 1D-Var retrievals examined. The two retrievals produce similar spatial patterns but with different magnitude. The allocation of a large amount of CWP in the 1D-Var retrieval seems to be related to the stratiform portion of rain, which is produced by the large-scale condensation scheme. To attain better-constrained cloud/rain ratios and improved retrieval quality, this study suggests the implementation of higher microwave frequency channels in the 1D-Var algorithm.

show abstract

“…As long as the errors are random rather than systematic, the poorer accuracy of cloud and precipitation radiative transfer can be accounted for with an observation error model that assigns bigger errors in cloudy and precipitating situations than in clear skies (e.g. Geer and Bauer, 2011). Furthermore, forecast models find it difficult to put cloud and precipitation in exactly the right place with the right intensity (e.g.…”

Section: Introductionmentioning

confidence: 99%

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

2014

View full text Add to dashboard Cite

Abstract. To simulate passive microwave radiances in allsky conditions requires better knowledge of the scattering properties of frozen hydrometeors. Typically, snow particles are represented as spheres and their scattering properties are calculated using Mie theory, but this is unrealistic and, particularly in deep-convective areas, it produces too much scattering in mid-frequencies (e.g. 30-50 GHz) and too little scattering at high frequencies (e.g. 150-183 GHz). These problems make it hard to assimilate microwave observations in numerical weather prediction (NWP) models, particularly in situations where scattering effects are most important, such as over land surfaces or in moisture sounding channels. Using the discrete dipole approximation to compute scattering properties, more accurate results can be generated by modelling frozen particles as ice rosettes or simplified snowflakes, though hexagonal plates and columns often give worse results than Mie spheres. To objectively decide on the best particle shape (and size distribution) this study uses global forecast departures from an NWP system (e.g. observation minus forecast differences) to indicate the quality of agreement between model and observations. It is easy to improve results in one situation but worsen them in others, so a rigorous method is needed: four different statistics are checked; these statistics are required to stay the same or improve in all channels between 10 GHz and 183 GHz and in all weather situations globally. The optimal choice of snow particle shape and size distribution is better across all frequencies and all weather conditions, giving confidence in its physical realism. Compared to the Mie sphere, most of the systematic error is removed and departure statistics are improved by 10 to 60 %. However, this improvement is achieved with a simple "one-size-fits-all" shape for snow; there is little additional benefit in choosing the particle shape according to the precipitation type. These developments have improved the accuracy of scattering radiative transfer sufficiently that microwave all-sky assimilation is being extended to land surfaces, to higher frequencies and to sounding channels.

show abstract

Observation errors in all‐sky data assimilation

Cited by 165 publications

References 44 publications

Flow-Dependent Reliability: A Path to More Skillful Ensemble Forecasts

Flow-Dependent Reliability: A Path to More Skillful Ensemble Forecasts

Comparing rain retrievals from GPROF with ECMWF 1D‐Var products

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

Contact Info

Product

Resources

About