Exploratory use of a satellite cloud mask to verify <scp>NWP</scp> models

Crocker, Ric; Mittermaier, Marion

doi:10.1002/met.1384

Cited by 13 publications

(15 citation statements)

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“…To overcome that weakness, an objective verification method can be used. This approach was initially developed for rainfall data and that is how it is commonly used, but it can be adopted to other applications, including cloudiness (CROCKER and MITTERMAIER 2013). In this approach, it is not grid cells, but objects, that are analyzed.…”

Section: Evaluation Of the Model Results Using The Cloud Cover Maskmentioning

confidence: 99%

“…After simplification, the approach can be successfully applied also for other binary variables, such as cloud mask, which has been done previously, for example, by CROCKER andMITTERMAIER (2013) or ZINGERLE andNURMI (2008).…”

Section: Objective Verificationmentioning

confidence: 99%

“…It is usually not a parameter produced directly by meteorological models, but requires additional post-processing from other model output variables. Much more straightforward approach is to use cloud mask, which can be easily derived from cloud fractions at model levels (CROCKER and MITTERMAIER 2013). Satellite cloud mask is derived from multiple spectral channels, usually based on visible light and supported by infrared wavelengths, through a series of cloud detection tests.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity Study of Cloud Cover and Ozone Modeling to Microphysics Parameterization

Wałaszek

Kryza

Szymanowski

et al. 2016

Pure Appl. Geophys.

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Abstract-Cloud cover is a significant meteorological parameter influencing the amount of solar radiation reaching the ground surface, and therefore affecting the formation of photochemical pollutants, most of all tropospheric ozone (O 3 ). Because cloud amount and type in meteorological models are resolved by microphysics schemes, adjusting this parameterization is a major factor determining the accuracy of the results. However, verification of cloud cover simulations based on surface data is difficult and yields significant errors. Current meteorological satellite programs provide many high-resolution cloud products, which can be used to verify numerical models. In this study, the Weather Research and Forecasting model (WRF) has been applied for the area of Poland for an episode of June 17th-July 4th, 2008, when high ground-level ozone concentrations were observed. Four simulations were performed, each with a different microphysics parameterization: Purdue Lin, Eta Ferrier, WRF Single-Moment 6-class, and Morrison Double-Moment scheme. The results were then evaluated based on cloud mask satellite images derived from SEVIRI data. Meteorological variables and O 3 concentrations were also evaluated. The results show that the simulation using Morrison Double-Moment microphysics provides the most and Purdue Lin the least accurate information on cloud cover and surface meteorological variables for the selected high ozone episode. Those two configurations were used for WRF-Chem runs, which showed significantly higher O 3 concentrations and better model-measurements agreement of the latter.

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Section: Evaluation Of the Model Results Using The Cloud Cover Maskmentioning

confidence: 99%

Section: Objective Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensitivity Study of Cloud Cover and Ozone Modeling to Microphysics Parameterization

Wałaszek

Kryza

Szymanowski

et al. 2016

Pure Appl. Geophys.

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“…Scale separation methods have been used by De Sales andLiu et al (2011), and field deformation approaches by Nan et al (2010). Feature-based techniques have been used by Demaria et al (2011), Hartung et al (2011), Johnson et al (2011, Gorgas and Dorninger (2012b), Wapler et al (2012), Crocker and Mittermaier (2013), Mittermaier and Bullock (2013), Weniger and Friederichs (2016), and Mittermaier et al (2016). The verification of forecasts at observing locations has been extended since papers by Theis et al (2005), Ben Bouallègue and Theis (2014), Mittermaier (2014), and Mittermaier and Csima (2017), who compared deterministic and ensembles forecasts on the kilometer scale.…”

Section: The Setup Of the Mesovict Projectmentioning

confidence: 99%

The Setup of the MesoVICT Project

Dorninger

Gilleland

Casati

et al. 2018

Bulletin of the American Meteorological Society

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MesoVICT focuses on the application, capability, and enhancement of spatial verification methods as applied to deterministic and ensemble forecasts of precipitation, wind, and temperature over complex terrain and includes observation uncertainty assessment. THE SETUP OF THE MesoVICT PROJECTManfreD Dorninger, eriC gillelanD, BarBara Casati, Marion P. MitterMaier, elizaBeth e. eBert, BarBara g. Brown, anD laurenCe J. wilson A s numerical weather prediction (NWP) models began to increase considerably in resolution, it became clear that traditional gridpoint-bygridpoint verification methods did not provide adequate or sufficient diagnostic information about forecast performance for some users (e.g., Mass et al. 2002). Double penalties arising from spatial displacement (or perhaps timing) errors and more rapid growth of small-scale errors often result in poorer performance scores for higher-resolution forecasts than their coarser counterparts, even when subjective evaluation would support the higher-resolution models as being better. Subsequently, a host of new verification methods were developed very rapidly (Ebert and McBride 2000;Harris et al. 2001;Casati et al. 2004;Nachamkin 2004;Davis et al. 2006;Keil and Craig 2007;Roberts and Lean 2008;Marzban et al. 2009;Gilleland et al. 2010b), which we will refer to as spatial methods. There still are gaps in our understanding when it comes to interpreting what all the new spatial methods tell us; gaining an in-depth understanding of forecast performance depends on grasping the full meaning of the verification results. Furthermore, the investment required to implement a new spatial method is relatively high compared to traditional verification methods, making it important to have some criteria on which to decide which methods would best suit a particular user's need(s). Therefore, a spatial methods meta-verification, or intercomparison, project was created to try to answer some of these questions.The first spatial verification methods intercomparison project (ICP;Gilleland et al. 2009Gilleland et al. , 2010a was initiated in 2007 with the aim of better understanding the rapidly increasing literature concerning new spatial verification methods, and several questions were addressed:• How does each method inform about forecast performance overall?• Which aspects of forecast error does each method best identify (e.g., location error, scale dependence of the skill, etc.)? • Which methods yield identical information to each other and which methods provide complementary information?The aim of the ICP was to analyze the behavior and provide a structured and systematic cataloging of the existing spatial verification methods, with the final goal of providing guidance to the users on which methods are best for which purpose. The project focused on prescribed errors for idealized cases and quantitative precipitation forecasts, where the ability of the verification methods to diagnose the known error was assessed (Ahijevych et al. 2009). For this first intercomparison, complex terrain ...

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“…(2009) a variety of spatial metrics including the FSS and the method of compositing described by Nachamkin (2004). Crocker and Mittermaier (2013) have applied the object-based Structure-Amplitude-Location (SAL) method (Wernli et al ., 2008). For an overview, and a first attempt at a classification of spatial verification methods the reader is referred to Gilleland et al .…”

Section: Introductionmentioning

confidence: 99%

Using MODE to explore the spatial and temporal characteristics of cloud cover forecasts from high‐resolution NWP models

Mittermaier

Bullock

2013

Meteorological Applications

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Cloud forecasts pose a particularly difficult challenge to verification through the three-dimensional structure of cloud. Two-dimensional total cloud forecasts from one convection-permitting and one near-convection-resolving Numerical Weather Prediction (NWP) model are compared to a cloud analysis using the Method for Object-based Diagnostic Evaluation (MODE) and MODE Time Domain (TD) which is currently being developed to also consider the time dimension. This study attempts to quantify the spatial and temporal evolution of specifically persistent (static) and systematic cloud scenarios where the cloud boundaries change only slowly. These scenarios can lead to significant cloud and temperature errors if the locations of these boundaries are not well forecast. MODE and MODE TD are used to identify and compare the number of cloud free objects, their evolution over time in terms of area or volume. The results show that including the time dimension provides a different perspective to the assessment, but that the choice of method-specific parameters is critical for enabling the matching of time objects. Crucially, model-relative behaviour can be isolated, and the specific characteristics of the cloud analyses are also quantifiable, and need to be accounted for when interpreting results.

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Exploratory use of a satellite cloud mask to verify NWP models

Cited by 13 publications

References 14 publications

Sensitivity Study of Cloud Cover and Ozone Modeling to Microphysics Parameterization

Sensitivity Study of Cloud Cover and Ozone Modeling to Microphysics Parameterization

The Setup of the MesoVICT Project

Using MODE to explore the spatial and temporal characteristics of cloud cover forecasts from high‐resolution NWP models

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