A critical assessment of surface cloud observations and their use for verifying cloud forecasts

Mittermaier, Marion

doi:10.1002/qj.1918

Cited by 34 publications

(46 citation statements)

References 34 publications

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“…Increasing the aggregation, the log‐odds ratio for an aggregation time interval of 90 min at 1.3 km spatial resolution is similar to that for a 60 min time integration at a 12 km resolution (≈1.95). The results shown so far point out that WRF appears to perform better for coarser model spatial or temporal resolutions in accordance with the results presented in other works [e.g., Mittermaier , ]. This may be considered disappointing and indicative of a rather poor WRF skill to properly represent local‐ or small‐area clouds.…”

Section: Resultsmentioning

confidence: 99%

“…This may be considered disappointing and indicative of a rather poor WRF skill to properly represent local‐ or small‐area clouds. Nevertheless, it is a consequence of the variation of the cloud predictability limit at different spatial resolutions and the so‐called double‐penalty effect , already reported in previous works [ Mass et al ., ; Rossa et al ., ; Mittermaier , ; Cintineo et al ., ]. The double penalty consists in that although the model may generate a cloud, if it is not generated at the right moment and at the right place, it will be considered as if it was not created even though the cloud might have been generated in the proximities and about the time of its actual position.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, they have an increased temporal resolution with respect to spaceborne sensors. In the latest years, many evaluation studies of cloud characteristics based on local ground retrievals have been conducted, including lidar and radar data [Guichard et al, 2003;Stephens et al, 2004;Wood et al, 2005;Morcrette et al, 2012] and manual synoptic observations [Mittermaier, 2012]. Some comprehensive evaluation studies have used both satellite estimates and ground measurement [Morcrette, 2002;Yoo et al, 2013].…”

Section: Introductionmentioning

confidence: 99%

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Macroscopic cloud properties in the WRF NWP model: An assessment using sky camera and ceilometer data

et al. 2015

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The ability of six microphysical parameterizations included in the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model to represent various macroscopic cloud characteristics at multiple spatial and temporal resolutions is investigated. In particular, the model prediction skills of cloud occurrence, cloud base height, and cloud cover are assessed. When it is possible, the results are provided separately for low‐, middle‐, and high‐level clouds. The microphysical parameterizations assessed are WRF single‐moment six‐class, Thompson, Milbrandt‐Yau, Morrison, Stony Brook University, and National Severe Storms Laboratory double moment. The evaluated macroscopic cloud properties are determined based on the model cloud fractions. Two cloud fraction approaches, namely, a binary cloud fraction and a continuous cloud fraction, are investigated. Model cloud cover is determined by overlapping the vertically distributed cloud fractions following three different strategies. The evaluation is conducted based on observations gathered with a ceilometer and a sky camera located in Jaén (southern Spain). The results prove that the reliability of the WRF model mostly depends on the considered cloud parameter, cloud level, and spatiotemporal resolution. In our test bed, it is found that WRF model tends to (i) overpredict the occurrence of high‐level clouds irrespectively of the spatial resolution, (ii) underestimate the cloud base height, and (iii) overestimate the cloud cover. Overall, the best cloud estimates are found for finer spatial resolutions (1.3 and 4 km with slight differences between them) and coarser temporal resolutions. The roles of the parameterization choice of the microphysics scheme and the cloud overlapping strategy are, in general, less relevant.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Macroscopic cloud properties in the WRF NWP model: An assessment using sky camera and ceilometer data

et al. 2015

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“…This might cause some small inconsistencies amongst different stations. Other sources of uncertainties in the comparisons are the subjectivity of human 25 observations and their inevitable variation from one observer to another (Mittermaier, 2012); the scenery effect (Malberg, 1973;Karlsson, 2003;Werkmeister et al, 2015) which increases the difficulty of comparing two different observation geometries, as cloud fractional cover tends to be overestimated by a ground-based observer looking in a slanted way at clouds spread vertically, especially when clouds are low on the horizon; and the detection difference between a human eye limited to the visible spectra and satellite sensors, which have wider spectral ranges, especially infrared wavelengths. …”

Section: Visual Observationsmentioning

confidence: 99%

Correction of CCI cloud data over the Swiss Alps using ground-based radiation measurements

Jeanneret¹,

Martucci²,

Pinnock³

et al. 2018

Preprint

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Abstract. The validation of long term cloud datasets retrieved from satellites is challenging due to their worldwide coverage going back as far as the 1980s, among others. A trustworthy reference cannot be found easily at every location and every time.Mountainous regions represent especially a problem since ground-based measurements are sparser. Moreover, as retrievals from passive satellite radiometers are difficult in winter due to the presence of snow on the ground, it is particularly important to develop new ways to evaluate and to correct satellite datasets over elevated areas. 5In winter for ground levels above 1000m (a.s.l.) in Switzerland, the cloud occurrence of the newly-released cloud property datasets of the ESA Climate Change Initiative Cloud_cci project (AVHRR-PM and MODIS-Aqua series) is 132 % to 217 % that of SYNOP observations, corresponding to between 24 % and 54 % of false cloud detections. Furthermore, the overestimations increase with the altitude of the sites and are associated with particular retrieved cloud properties.In this study, a novel post-processing approach is proposed to reduce the amount of false cloud detections in the satellite 10 datasets. A combination of ground-based downwelling longwave and shortwave radiation and temperature measurements is used to obtain a mask for the cloud cover above 41 locations in Switzerland. An agreement of 85 % is obtained when the cloud cover is compared to surface synoptic observations (90 % within ± 1 okta difference). The obtained cloud mask has been co-located with the satellite observations and a decision tree is trained to automatically detect the overestimations in the satellite's cloud masks. Cross-validated results show that 62 ± 13 % of these overestimations can be identified by the model, 15reducing the systematic error in the satellite datasets to 4.3 ± 2.8 %, at the cost of an increase of 7 ± 2 % of missed clouds.Using this model, it is hence possible to significantly improve the cloud detection reliability in elevated areas in the Cloud_cci's AVHRR-PM and MODIS-Aqua products.

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“…This feature seems to be replicated best by the JMA model (vertical panel in Figure (b)), except that one of the peaks has shifted from 1 to 0 okta. This shift may not seem significant at first glance, but as Mittermaier () explains, cloud amounts are rarely considered in isolation but are usually taken as being greater than a defined amount, thus this will have an impact on the verification scores examined below.…”

Section: Low‐to‐medium Cloud Verificationmentioning

confidence: 99%

Point verification and improved communication of the low‐to‐medium cloud cover forecasts

Tam

Wong

2017

Meteorological Applications

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The amount of sunshine weighs heavily in our perception of the weather. It is largely determined by cloud cover, especially that at the low‐to‐medium level. Therefore, when reviewing the Hong Kong Observatory's weather symbol forecasting product, verification on the low‐to‐medium cloud field is carried out against the synoptic observations at the Hong Kong International Airport. Several metrics are used to examine the different aspects of the forecasts, and consideration is given to the non‐Gaussian nature of the reported cloud amount for a fairer assessment. Based on the data from January to mid‐August 2015, the median of the forecasts from the European Centre for Medium‐Range Weather Forecasts Ensemble Prediction System is found to outperform the other model forecasts, particularly when the performance is examined by forecast day. This paper presents these results and also discusses the potential of using the field in deriving site‐specific weather symbol forecasts.

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A critical assessment of surface cloud observations and their use for verifying cloud forecasts

Cited by 34 publications

References 34 publications

Macroscopic cloud properties in the WRF NWP model: An assessment using sky camera and ceilometer data

Macroscopic cloud properties in the WRF NWP model: An assessment using sky camera and ceilometer data

Correction of CCI cloud data over the Swiss Alps using ground-based radiation measurements

Point verification and improved communication of the low‐to‐medium cloud cover forecasts

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