Theo Brandsma

We provide a long-term evaluation of compact, all-in-one automatic weather stations (AiOWS) compared to professional-grade Automatic Weather Stations (AWS). We examine the performance, longevity, and degradation of six AiO WS models over several years of non serviced use. The objective was to determine how closely these low-cost stations meet World Meteorological Organization (WMO) performance standards for temperature, humidity, wind, and precipitation, and to identify their weaknesses and maintenance needs. Previous studies show the potential value of AiOWS when data are properly quality-controlled, yet long-term reliability remains uncertain. To address this we deployed six AiO WS units— Davis VVue, Davis VP2, METER ATMOS41, Lufft WS601, and Vaisala WXT520, alongside two collocated reference AWS meeting WMO standards. Before field installation, each unit was tested in (KNMI’s) calibration lab for baseline validation. The stations were then operated in open terrain for multiple years without any servicing, simulating typical end-user neglect. Initially, all AiO WS met manufacturer specifications. After long-term exposure, however, sensors displayed varied durability. The Vaisala unit operated continuously for over 13 years, while others failed between four and seven years due to corrosion, component wear, and sensor drift. The METER and Davis VVue remained mostly functional but with degraded performance, whereas both Davis VP2 rain gauges failed early due to reed switch damage. Temperature measurements were the most robust. In climate chamber tests, new and aged sensors maintained accuracy within ±0.3 °C across -15 °C to 30 °C, drifting slightly (underestimating by 0.5–0.7 °C) above 30 °C. Field data confirmed these results, though strong solar radiation caused overestimations during summer. The Vaisala and Davis VVue units remained within WMO Class B limits after a decade. Relative humidity showed consistent deterioration. Most sensors overestimated low humidity and underestimated above 90 %, particularly the METER unit, whose bias grew markedly after five years. Wind speed accuracy degraded due to mechanical wear. Cup anemometers underreported low winds and failed completely in some cases. Sonic sensors (Vaisala, METER) produced erratic readings after several years, highlighting their fragility outdoors. Precipitation performance was weakest across all models. Tipping bucket designs suffered from clogging, internal corrosion, and undercatch errors, while haptic or drip-based sensors became inaccurate as components aged or fouled. We concluded that compact AiO WS can provide scientifically useful temperature data if properly managed but fall short for humidity, wind, and particularly precipitation unless regularly serviced. Long-term unattended operation severely limits reliability, yet moderate maintenance can potentially restore performance close to WMO Class A/B standards, extending their utility for dense observation networks.

We provide a long-term evaluation of compact, all-in-one automatic weather stations (AiOWS) compared to professional-grade Automatic Weather Stations (AWS). We examine the performance, longevity, and degradation of six AiO WS models over several years of non serviced use. The objective was to determine how closely these low-cost stations meet World Meteorological Organization (WMO) performance standards for temperature, humidity, wind, and precipitation, and to identify their weaknesses and maintenance needs. Previous studies show the potential value of AiOWS when data are properly quality-controlled, yet long-term reliability remains uncertain. To address this we deployed six AiO WS units— Davis VVue, Davis VP2, METER ATMOS41, Lufft WS601, and Vaisala WXT520, alongside two collocated reference AWS meeting WMO standards. Before field installation, each unit was tested in (KNMI’s) calibration lab for baseline validation. The stations were then operated in open terrain for multiple years without any servicing, simulating typical end-user neglect. Initially, all AiO WS met manufacturer specifications. After long-term exposure, however, sensors displayed varied durability. The Vaisala unit operated continuously for over 13 years, while others failed between four and seven years due to corrosion, component wear, and sensor drift. The METER and Davis VVue remained mostly functional but with degraded performance, whereas both Davis VP2 rain gauges failed early due to reed switch damage. Temperature measurements were the most robust. In climate chamber tests, new and aged sensors maintained accuracy within ±0.3 °C across -15 °C to 30 °C, drifting slightly (underestimating by 0.5–0.7 °C) above 30 °C. Field data confirmed these results, though strong solar radiation caused overestimations during summer. The Vaisala and Davis VVue units remained within WMO Class B limits after a decade. Relative humidity showed consistent deterioration. Most sensors overestimated low humidity and underestimated above 90 %, particularly the METER unit, whose bias grew markedly after five years. Wind speed accuracy degraded due to mechanical wear. Cup anemometers underreported low winds and failed completely in some cases. Sonic sensors (Vaisala, METER) produced erratic readings after several years, highlighting their fragility outdoors. Precipitation performance was weakest across all models. Tipping bucket designs suffered from clogging, internal corrosion, and undercatch errors, while haptic or drip-based sensors became inaccurate as components aged or fouled. We concluded that compact AiO WS can provide scientifically useful temperature data if properly managed but fall short for humidity, wind, and particularly precipitation unless regularly serviced. Long-term unattended operation severely limits reliability, yet moderate maintenance can potentially restore performance close to WMO Class A/B standards, extending their utility for dense observation networks.

We provide a long-term evaluation of compact, all-in-one automatic weather stations (AiOWS) compared to professional-grade Automatic Weather Stations (AWS). We examine the performance, longevity, and degradation of six AiO WS models over several years of non serviced use. The objective was to determine how closely these low-cost stations meet World Meteorological Organization (WMO) performance standards for temperature, humidity, wind, and precipitation, and to identify their weaknesses and maintenance needs. Previous studies show the potential value of AiOWS when data are properly quality-controlled, yet long-term reliability remains uncertain. To address this we deployed six AiO WS units— Davis VVue, Davis VP2, METER ATMOS41, Lufft WS601, and Vaisala WXT520, alongside two collocated reference AWS meeting WMO standards. Before field installation, each unit was tested in (KNMI’s) calibration lab for baseline validation. The stations were then operated in open terrain for multiple years without any servicing, simulating typical end-user neglect. Initially, all AiO WS met manufacturer specifications. After long-term exposure, however, sensors displayed varied durability. The Vaisala unit operated continuously for over 13 years, while others failed between four and seven years due to corrosion, component wear, and sensor drift. The METER and Davis VVue remained mostly functional but with degraded performance, whereas both Davis VP2 rain gauges failed early due to reed switch damage. Temperature measurements were the most robust. In climate chamber tests, new and aged sensors maintained accuracy within ±0.3 °C across -15 °C to 30 °C, drifting slightly (underestimating by 0.5–0.7 °C) above 30 °C. Field data confirmed these results, though strong solar radiation caused overestimations during summer. The Vaisala and Davis VVue units remained within WMO Class B limits after a decade. Relative humidity showed consistent deterioration. Most sensors overestimated low humidity and underestimated above 90 %, particularly the METER unit, whose bias grew markedly after five years. Wind speed accuracy degraded due to mechanical wear. Cup anemometers underreported low winds and failed completely in some cases. Sonic sensors (Vaisala, METER) produced erratic readings after several years, highlighting their fragility outdoors. Precipitation performance was weakest across all models. Tipping bucket designs suffered from clogging, internal corrosion, and undercatch errors, while haptic or drip-based sensors became inaccurate as components aged or fouled. We concluded that compact AiO WS can provide scientifically useful temperature data if properly managed but fall short for humidity, wind, and particularly precipitation unless regularly serviced. Long-term unattended operation severely limits reliability, yet moderate maintenance can potentially restore performance close to WMO Class A/B standards, extending their utility for dense observation networks.

Mobile temperature and humidity measurements have been performed along a 14-km transect through the city of Utrecht, in the Netherlands (311 000 inhabitants), during the period March 2006–January 2009. The measurements took place on a bicycle during commuter traffic and resulted in 106 nighttime profiles (before sunrise) and 77 daytime (afternoon) profiles. It is shown how the intensity of the urban heat island depends on wind direction, cloudiness, and wind speed. Statistical models are constructed that relate the mean and maximum nighttime urban heat island intensity profiles to area-averaged sky-view factors and land use combined at both the micro- and local scales. Sky-view factors are estimated from a 0.5 m × 0.5 m surface elevation database, and land use is obtained from a 25 m × 25 m land-use database. The models are calibrated using the mobile measurements and provide estimates of the spatial distribution of the mean and maximum nighttime urban heat island intensity in Utrecht. Both models explain more than 75% of the variance. A separate nonlinear model is introduced that relates the temperature differences between the warmest part and coolest part of the transects to wind speed and cloudiness.

A method has been developed that largely automates the labor-intensive extraction work for large amounts of rainfall strip charts and paper rolls. The method consists of the following five basic steps: 1) scanning the charts and rolls to high-resolution digital images, 2) manually and visually registering relevant meta information from charts and rolls and preprocessing rolls to locate day transitions, 3) applying automatic curve extraction software in a batch process to determine the coordinates of cumulative rainfall lines on the images, 4) postprocessing the curves that were not correctly determined in step 3, and 5) aggregating the cumulative rainfall in pixel coordinates to the desired time resolution. The core of the method is in step 3. Here a color detection procedure is introduced that automatically separates the background of the charts and rolls from the grid and subsequently the rainfall curve. The rainfall curve is detected by minimization of a cost function. In total, 321 station years of locations in the Netherlands have successfully been digitized and transformed to long-term rainfall time series with 5-min resolution. In about 30% of the cases, semiautomatic postprocessing of the results was needed using a purpose-built graphical interface application. This percentage, however, strongly depends on the quality of the recorded curves and the charts and rolls. Although developed for rainfall, the method can be applied to other elements as well.

The influence of urban heat advection on the temperature time series of the Dutch GCOS station De Bilt has been studied empirically by comparing the hourly meteorological observations (1993–2000) with those of the nearby (7.5 km) rural station at Soesterberg. Station De Bilt is in the transition zone (TZ) between the urban and rural area, being surrounded by three towns, Utrecht, De Bilt and Zeist. The dependence of the hourly temperature differences between De Bilt and Soesterberg on wind direction has been examined as a function of season, day‐ and night‐time hours and cloud amount. Strong dependence on wind direction was apparent for clear nights, with the greatest effects (up to 1 °C on average) for wind coming from the towns. The magnitude of the effect decreased with increasing cloudiness. The analysis suggests that most of the structure in the wind direction dependence is caused by urban heat advection to the measuring site in De Bilt. The urban heat advection is studied in more detail with an additive statistical model. Because the urban areas around the site expanded in the past century, urban heat advection trends contaminate the long‐term trends in the temperature series (1897–present) of De Bilt. Based on the present work, we estimate that this effect may have raised the annual mean temperatures of De Bilt by 0.10 ± 0.06 °C during the 20th century, being almost the full value of the present‐day urban heat advection. The 0.10 ± 0.06 °C rise due to urban heat advection corresponds to about 10% of the observed temperature rise of about 1.0 °C in the last century. Copyright © 2003 Royal Meteorological Society