Present weather: comparing human observations and one type of automated sensor

Merenti-Välimäki, Hanna-Leena; Lönnqvist, Jan; Laininen, Pertti

doi:10.1017/s1350482701004108

Cited by 16 publications

(12 citation statements)

References 3 publications

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“…Manual observations have an advantage compared to automatic weather observations: if several weather phenomena are occurring simultaneously they all can be documented. Automatic weather stations may have problems in such a situation, occasionally resulting in false detection of present weather, especially in the case of low intensity precipitation such as drizzle (Merenti‐Välimäki et al , 2001).…”

Section: Methodsmentioning

confidence: 99%

Analysis of weather factors responsible for the traffic ‘Black Day’ in Helsinki, Finland, on 17 March 2005

Juga

Hippi

Moisseev

et al. 2011

Meteorological Applications

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During the morning rush hours of 17 March 2005, a band of intense snowfall affected the Helsinki metropolitan area in southern Finland. The event caused severe pile-ups on the highways, with almost 300 crashed cars, the deaths of three people and more than 60 people injured. The snowfall was soon followed by freezing drizzle. Some of the media later blamed this as being responsible for the unprecedented number and severity of the accidents that had occurred. However, the official inquiry came to the conclusion that the pile-ups had been caused by the very poor visibility due to intense snowfall and excessive driving speeds combined with reduced road surface friction. In this study, these two viewpoints are investigated, by using high time-resolution dual-polarization radar observations to analyse the changes in intensity and form of the precipitation leading to the event. The radar data were particularly useful in supplementing weather observations.

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Section: Methodsmentioning

confidence: 99%

Analysis of weather factors responsible for the traffic ‘Black Day’ in Helsinki, Finland, on 17 March 2005

Juga

Hippi

Moisseev

et al. 2011

Meteorological Applications

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“…ROS days classified from the satellite record were validated using in situ weather observations from collocated climate stations to identify if rain occurred either the day of or the day before (i−1) a classified ROS event (Obs rain ), or if the observed station precipitation was null (Obs null ). Given the limitations of wintertime precipitation measurements (Merenti-Valimaki 2001, Martinaitis et al 2015, Grossi et al 2017, Obs null included conditions where either no precipitation was measured or there was no effective precipitation measurement. Three temperature-driven variables were therefore created and used as a proxy for the rainfall observations (Obs rain ), which can have large measurement uncertainty during freeze-thaw transitions (Martinaitis et al 2015).…”

Section: Tier 2-climate Observation Networkmentioning

confidence: 99%

Rain-on-snow events in Alaska, their frequency and distribution from satellite observations

Pan

Kirchner

Kimball

et al. 2018

Environ. Res. Lett.

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Wet snow and the icing events that frequently follow wintertime rain-on-snow (ROS) affect high latitude ecosystems at multiple spatial and temporal scales, including hydrology, carbon cycle, wildlife, and human development. However, the distribution of ROS events and their response to climatic changes are uncertain. In this study, we quantified ROS spatiotemporal variability across Alaska during the cold season (November to March) and clarified the influence of precipitation and temperature variations on these patterns. A satellite-based daily ROS geospatial classification was derived for the region by combining remote sensing information from overlapping MODIS and AMSR sensor records. The ROS record extended over the recent satellite record (water years 2003-2011 and 2013-2016) and was derived at a daily time step and 6 km grid, benefiting from finer (500 m) resolution MODIS snow cover observations and coarser (12.5 km) AMSR microwave brightness temperature-based freeze-thaw retrievals. The classification showed favorable ROS detection accuracy (75%-100%) against in situ climate observations across Alaska. Pixel-wise correlation analysis was used to clarify relationships between the ROS patterns and underlying physiography and climatic influences. Our findings indicate that cold season ROS events are most common during autumn and spring months along the maritime Bering Sea coast and boreal interior regions, but are infrequent on the colder arctic North Slope. The frequency and extent of ROS events coincided with warm temperature anomalies (p < 0.1), but showed a generally weaker relationship with precipitation. The weaker precipitation relationship was attributed to several factors, including large uncertainty in cold season precipitation measurements, and the important contribution of humidity and turbulent energy transfer in driving snowmelt and icing events independent of rainfall. Our results suggest that as high latitude temperatures increase, wet snow and ROS events will also increase in frequency and extent, particularly in the southwestern and interior regions of Alaska.

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“…Efforts to automatize present weather observations impose high requirements on instruments such as present weather sensors. Automated present weather sensors encounter problems at temperatures around 0 • C as well as for light precipitation and small particle sizes (Merenti-Välimäki et al, 2001). High wind speed also complicates the PP determination because the wind speed strongly interferes with the particle fall speed that solely carries the PP information.…”

Section: Introductionmentioning

confidence: 99%

An automatic precipitation-phase distinction algorithm for optical disdrometer data over the global ocean

2016

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Abstract. The lack of high-quality in situ surface precipitation data over the global ocean so far limits the capability to validate satellite precipitation retrievals. The first systematic ship-based surface precipitation data set OceanRAIN (Ocean Rainfall And Ice-phase precipitation measurement Network) aims at providing a comprehensive statistical basis of in situ precipitation reference data from optical disdrometers at 1 min resolution deployed on various research vessels (RVs). Deriving the precipitation rate for rain and snow requires a priori knowledge of the precipitation phase (PP). Therefore, we present an automatic PP distinction algorithm using available data based on more than 4 years of atmospheric measurements onboard RV Polarstern that covers all climatic regions of the Atlantic Ocean. A time-consuming manual PP distinction within the OceanRAIN post-processing serves as reference, mainly based on 3-hourly present weather information from a human observer. For automation, we find that the combination of air temperature, relative humidity, and 99th percentile of the particle diameter predicts best the PP with respect to the manually determined PP. Excluding mixed phase, this variable combination reaches an accuracy of 91 % when compared to the manually determined PP for 149 635 min of precipitation from RV Polarstern. Including mixed phase (165 632 min), an accuracy of 81.2 % is reached for two independent PP distributions with a slight snow overprediction bias of 0.93. Using two independent PP distributions represents a new method that outperforms the conventional method of using only one PP distribution to statistically derive the PP. The new statistical automatic PP distinction method considerably speeds up the data post-processing within OceanRAIN while introducing an objective PP probability for each PP at 1 min resolution.

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Present weather: comparing human observations and one type of automated sensor

Cited by 16 publications

References 3 publications

Analysis of weather factors responsible for the traffic ‘Black Day’ in Helsinki, Finland, on 17 March 2005

Analysis of weather factors responsible for the traffic ‘Black Day’ in Helsinki, Finland, on 17 March 2005

Rain-on-snow events in Alaska, their frequency and distribution from satellite observations

An automatic precipitation-phase distinction algorithm for optical disdrometer data over the global ocean

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