Cloud height measurement by a network of all-sky imagers

Abstract. Cloud base height (CBH) is an important parameter for many applications such as aviation, climatology or solar irradiance nowcasting (forecasting for the next seconds to hours ahead). The latter application is of increasing importance for the operation of distribution grids and photovoltaic power plants, energy storage systems and flexible consumers. To nowcast solar irradiance, systems based on all-sky imagers (ASIs), cameras monitoring the entire sky dome above their point of installation, have been demonstrated. Accurate knowledge of the CBH is required to nowcast the spatial distribution of solar irradiance around the ASI's location at a resolution down to 5 m. To measure the CBH, two ASIs located at a distance of usually less than 6 km can be combined into an ASI pair. However, the accuracy of such systems is limited. We present and validate a method to measure the CBH using a network of ASIs to enhance accuracy. To the best of our knowledge, this is the first method to measure the CBH with a network of ASIs which is demonstrated experimentally. In this study, the deviations of 42 ASI pairs are studied in comparison to a ceilometer and are characterized by camera distance. The ASI pairs are formed from seven ASIs and feature camera distances of 0.8…5.7 km. Each of the 21 tuples of two ASIs formed from seven ASIs yields two independent ASI pairs as the ASI used as the main and auxiliary camera, respectively, is swapped. Deviations found are compiled into conditional probabilities that tell how probable it is to receive a certain reading of the CBH from an ASI pair given that the true CBH takes on some specific value. Based on such statistical knowledge, in the inference, the likeliest actual CBH is estimated from the readings of all 42 ASI pairs. Based on the validation results, ASI pairs with a small camera distance (especially if <1.2 km) are accurate for low clouds (CBH<4 km). In contrast, ASI pairs with a camera distance of more than 3 km provide smaller deviations for greater CBH. No ASI pair provides the most accurate measurements under all conditions. The presented network of ASIs at different distances proves that, under all cloud conditions, the measurements of the CBH are more accurate than using a single ASI pair.

[1]  Luis Marroyo,et al.  The potential of forecasting in reducing the LCOE in PV plants under ramp-rate restrictions , 2019 .

[2]  Robert A. Taylor,et al.  Direct normal irradiance forecasting and its application to concentrated solar thermal output forecasting - A review , 2014 .

[3]  Dong Huang,et al.  3D cloud detection and tracking system for solar forecast using multiple sky imagers , 2015 .

[4]  George A. Isaac,et al.  The Canadian Airport Nowcasting System (CAN‐Now) , 2014 .

[5]  Lara Cook,et al.  Forecast-Based Decision Support for San Francisco International Airport: A NextGen Prototype System That Improves Operations during Summer Stratus Season , 2012 .

[6]  Yoav Y. Schechner,et al.  Distributed Sky Imaging Radiometry and Tomography , 2020, 2020 IEEE International Conference on Computational Photography (ICCP).

[7]  Robert Pitz-Paal,et al.  Applying self-supervised learning for semantic cloud segmentation of all-sky images , 2021, Atmospheric Measurement Techniques.

[8]  Steven Platnick,et al.  Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms , 2014 .

[9]  P. Bland,et al.  How to build a continental scale fireball camera network , 2017 .

[10]  Giovanni Martucci,et al.  Detection of Cloud-Base Height Using Jenoptik CHM15K and Vaisala CL31 Ceilometers , 2010 .

[11]  Cyrill Stachniss,et al.  Cloud photogrammetry with dense stereo for fisheye cameras , 2016 .

[12]  Robert Pitz-Paal,et al.  Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts , 2019, Remote. Sens..

[13]  Josep Calbó,et al.  Behavior of cloud base height from ceilometer measurements , 2013 .

[14]  Tomasz Bieliński.  A Parallax Shift Effect Correction Based on Cloud Height for Geostationary Satellites and Radar Observations , 2020, Remote. Sens..

[15]  Steven D. Miller,et al.  Cloud-Base Height Estimation from VIIRS. Part II: A Statistical Algorithm Based on A-Train Satellite Data , 2017 .

[16]  Albert Ansmann,et al.  Ceilometer lidar comparison: backscatter coefficient retrieval and signal-to-noise ratio determination , 2010 .

[17]  Stefan Wilbert,et al.  Short-term forecasting of high resolution local DNI maps with multiple fish-eye cameras in stereoscopic mode , 2017 .

[18]  Robert Pitz-Paal,et al.  Cloud height and tracking accuracy of three all sky imager systems for individual clouds , 2019, Solar Energy.

[19]  Robin J. Hogan,et al.  Verification of cloud‐fraction forecasts , 2009 .

[20]  H. Pedro,et al.  Benefits of solar forecasting for energy imbalance markets , 2016 .

[21]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[22]  William L. Smith,et al.  Fusion of surface ceilometer data and satellite cloud retrievals in 2D mesh interpolating model with clustering , 2019, Remote Sensing.

[23]  L. Ramírez,et al.  Benchmarking three low-cost, low-maintenance cloud height measurement systems and ECMWF cloud heights against a ceilometer , 2018, Solar Energy.

[24]  R. Pitz-Paal,et al.  Determination of cloud transmittance for all sky imager based solar nowcasting , 2019, Solar Energy.

[25]  L. Zarzalejo,et al.  Optimization of parabolic trough power plant operations in variable irradiance conditions using all sky imagers , 2020 .

[26]  Jan Kleissl,et al.  Cloud tomography applied to sky images: A virtual testbed , 2018, Solar Energy.

[27]  Jan Kleissl,et al.  Cloud base height from sky imager and cloud speed sensor , 2016 .

[28]  W. Philip Kegelmeyer,et al.  The Computation of Cloud-Base Height from Paired Whole-Sky Imaging Cameras , 1996 .

[29]  Robert Pitz-Paal,et al.  Nowcasting of DNI Maps for the Solar Field Based on Voxel Carving and Individual 3D Cloud Objects from All Sky Images , 2018 .

[30]  Achim Roth,et al.  Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data , 2018 .

[31]  Robert Pitz-Paal,et al.  Determination of the optimal camera distance for cloud height measurements with two all-sky imagers , 2019, Solar Energy.

[32]  Thomas Luhmann,et al.  Nahbereichsphotogrammetrie : Grundlagen, Methoden und Anwendungen , 2000 .

[33]  Detlev Heinemann,et al.  Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts , 2015 .

[34]  Jan Kleissl,et al.  Stereographic methods for cloud base height determination using two sky imagers , 2014 .

[35]  L. Zarzalejo,et al.  Evaluation of an all sky imager based nowcasting system for distinct conditions and five sites , 2020 .

[36]  Saifur Rahman,et al.  Distribution Voltage Regulation Through Active Power Curtailment With PV Inverters and Solar Generation Forecasts , 2017, IEEE Transactions on Sustainable Energy.

[37]  Hartwig Deneke,et al.  The HD(CP)2 Observational Prototype Experiment (HOPE) - An overview , 2016 .

[38]  Robert Pitz-Paal,et al.  Validation of an all‐sky imager–based nowcasting system for industrial PV plants , 2018 .

[39]  Ina Mattis,et al.  Evaluation of ECMWF-IFS (version 41R1) operational model forecasts of aerosol transport by using ceilometer network measurements , 2018, Geoscientific Model Development.

[40]  Stefan Winkler,et al.  Cloud base height estimation using high-resolution whole sky imagers , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[41]  Roland Siegwart,et al.  A Toolbox for Easily Calibrating Omnidirectional Cameras , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.