Intercomparison of snowfall measurements using disdrometers in two mountainous environments: Weissfluhjoch (Switzerland) and Formigal-Sarrios (Spain)

Comunicación presentada en: TECO-2016 (Technical Conference on Meteorological and Environmental Instruments and Methods of Observation) celebrada en Madrid, del 27 al 30 de septiembre de 2016.One of the objectives of the WMO/CIMO Solid Precipitation Intercomparison Experiment (SPICE) is to assess the performance of emerging technologies such as disdrometers for the measurement of solid precipitation. Numerous studies have assessed the performance of disdrometers for liquid precipitation, but the experience of using such instruments for solid precipitation is still limited. Among others, the Spanish site at Formigal and the Swiss site at Weissfluhjoch were built with very similar design (especially the reference measurement setting in a Double Fence Intercomparison Reference (DFIR)). Moreover, the environment of both sites (siting) is similar. This work evaluates the potential use of disdrometers for solid precipitation measurement in a mountainous environment. At each site two LPM Thies disdrometers, one shielded in a DFIR and the other one outside (with or without a Thies wind shield), are intercompared under different weather conditions (wind speed and direction, temperature and snowfall intensity) against the SPICE reference measurement using a weighing gauge (OTT Pluvio2 in a DFIR). This study will present preliminary results from both sites and will give first conclusion on the impact of various external parameters (such as wind and temperature) on the disdrometer snow accumulation measurement in and outside the DFIR, and with and without a Thies shield. Moreover, new lines of research are recommended in order to better understand the instrument and the raw data output

Measuring solid precipitation using heated tipping bucket gauges: an overview of performance and recommendations from WMO‐SPICE

Comunicación presentada en: TECO-2016 (Technical Conference on Meteorological and Environmental Instruments and Methods of Observation) celebrada en Madrid, del 27 al 30 de septiembre de 2016

Errors and adjustments for WMO-SPICE tipping-bucket precipitation gauges

Presentación realizada en: 19th Symposium on Meteorological Observation and Instrumentation celebrado del 7 al 11 de enero de 2018 en Austin, Texas

The WMO SPICE snow-on-ground intercomparison: an overview of sensor assessment and recommendations on best practices

Comunicación presentada en: TECO-2016 (Technical Conference on Meteorological and Environmental Instruments and Methods of Observation) celebrada en Madrid, del 27 al 30 de septiembre de 2016.One of the objectives of the WMO Solid Precipitation Intercomparison Experiment (SPICE) was to assess the performance and capabilities of automated sensors for measuring snow on the ground (SoG), including sensors that measure snow depth and snow water equivalent (SWE). The intercomparison focused on five snow depth sensors (models SHM30, SL300, SR50A, FLS-CH 10 and USH-8) and two SWE sensors (models CS725 and SSG1000) over two winter seasons (2013/2014 and 2014/2015). A brief discussion of the measurement reference(s) and an example of the intercomparisons are included. Generally, each of the sensors under test operated according to the manufacturer’s specifications and compared well with the site references, exhibiting high correlations with both the manual and automated reference measurements. The use of natural and artificial surface targets under snow depth sensors were examined in the context of providing a stable and representative surface for snow depth measurements. An assessment of sensor derived measurement quality and sensor return signal strength, where available as an output option, were analysed to help explain measurement outliers and sources of uncertainty with the goal of improving data quality and maximizing the sensor capabilities. Finally, where possible, relationships are established between the gauge measurement of solid precipitation and the measurement of snow on the ground. This paper will provide a brief summary of these results with more detail included in the WMO SPICE Final Report

Errors, biases, and corrections for weighing gauge precipitation measurements from WMO-SPICE

Presentación realizada en: American Meteorological Society Annual Meeting (2017) celebrado en Seattle (Washington) del 22 al 26 de enero de 2017

Errors, Biases, and Corrections for Weighing Gauge Precipitation Measurements from the WMO Solid Precipitation Intercomparison Experiment

Comunicación presentada en: TECO-2016 (Technical Conference on Meteorological and Environmental Instruments and Methods of Observation) celebrada en Madrid, del 27 al 30 de septiembre de 2016.Although precipitation has been measured for many centuries, precipitation measurements are still beset with significant biases and errors. Solid precipitation is particularly difficult to measure accurately, and biases between winter-time precipitation measurements from different measurement networks or different regions can exceed 100%. Using precipitation gauge results from the WMO Solid Precipitation Intercomparison Experiment (WMO-SPICE), errors in precipitation measurement caused by gauge uncertainty, spatial variability in precipitation, hydrometeor type, and wind are quantified. The methods used to calculate gauge catch efficiency and correct known biases are described briefly. Transfer functions describing catch efficiency as a function of air temperature and wind speed are also presented. In addition, the biases and errors associated with the use of a single transfer function to correct gauge undercatch at multiple sites are discussed

Tropical Instability Waves in the Atlantic Ocean: investigating the relative role of sea surface salinity and temperature from 2010 to 2018

International audienc

Rainfall imprint on SMOS and SMAP Sea Surface Salinity

International audienceTwo L-Band (1.4GHz) microwave radiometer missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP), currently provide sea-surface salinity (SSS) measurements. At this frequency, salinity is measured in the first centimetre below the sea surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS and SMAP SSS measurements, and the RR from the Special Sensor Microwave Imager/Sounder (SSMIS). We look at the robustness of the relationship in various areas. It is then used to estimate RR from SMOS and SMAP SSS measurements. By applying this algorithm over the global ocean between 30°S and 30°N, we found that the rain imprint is the dominant factor affecting SMOS and SMAP variability at small temporal scale, except in river plumes (Amazon, Mississippi, etc.) and in regions with high mesoscale variability. Our study allows to identify the observed difference between Argo products and satellite salinity that are due to the impact of rain on the satellite salinity in the first centimetre measured

What can we learn on rainfall from SMOS Sea Surface Salinity?

International audienceThe Soil Moisture and Ocean Salinity (SMOS) satellite mission has been measuring sea surface salinity (SSS) for over 6 years with about 5-day global ocean coverage and a spatial resolution of about 50 km. In rainy regions, at local and short time scales, the spatio-temporal variability of SSS is dominated by rainfall. The relationship between surface freshening and rain rate (RR) has been highlighted in the Pacific intertropical convergence zone (Boutin et al., 2014). In this context, this study investigates the rainfall characteristics that may be inferred from SMOS SSS based on statistical approach.Salinity anomalies associated with rainfall events are first estimated. In order to do so, a reference salinity (i.e. with no rain-induced signal) is computed for each pixel of the SMOS observation using the statistical distribution within 3°x3° region of SMOS SSS. In case the distribution is asymmetrical toward low values, suggesting a rain influence, a mean ‘non-rainy’ SSS corresponding to a Gaussian distribution fitted onto the highest part of the distribution (quantile>0.8) is computed. Rain rate probability associated with SSS anomalies are then inferred from a probabilistic approach. It also enables us to separate the rain intensity depending on the SSS anomaly. Finally, a RR retrieval algorithm based on SSS is developed combining this dependence with the SSS-RR relationship described in Boutin et al. (2014) and a spatial association index (spatial correlations of SSS anomalies within 100 km). SMOS-derived RRs are then collocated with various radiometers and CMORPH RR datasets. Their consistency is assessed.A particular focus will be put on RRs estimates derived during the Salinity Processes in the Upper Ocean Regional Study (SPURS-2, http://spurs2.jpl.nasa.gov) from a near real time implementation of rain retrieval from SMOS SSS.Boutin et al. (2014), Sea surface salinity under rain cells: SMOS satellite and in situ drifters observations, JGR: Oceans, doi:10.1002/2014JC01007

Precipitation estimates from L-Band Radiometer Sea Surface Salinity

International audienceThe Soil Moisture and Ocean Salinity (SMOS) satellite mission measures sea surface salinity (SSS) since 2010 with a spatial resolution of about 50 km. Since 2015, Soil Moisture Active and Passive (SMAP) mission also pro- vides SSS with a similar resolution. In rainy regions, at local and short time scales, the spatio-temporal variability of SSS is dominated by rainfall. The relationship between sea surface freshening and rain rate (RR) has been high- lighted in the Pacific intertropical convergence zone (Boutin et al., JGR, 2014). This study investigates the rainfall characteristics that may be inferred from SMOS and SMAP SSS based on a statistical approach, and to which extent this information is complementary to IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement mission) interpolated product. The IMERG algorithm intercalibrates, merges and interpolates “all” satellite passive microwave precipitation estimates (RPMW), together with microwave-calibrated infrared (IR) satellite estimates (RIR ) (Huffman et al., 2015) . The product contains the merged RR (Rm P M W ) as well as the RPMW and RIR individual estimates used by the algorithm.Salinity anomalies (∆S) associated with rainfall events are first estimated. A reference salinity (i.e. an estimate of the salinity preceding the rainfall event) is inferred from the SSS statistical distribution within 3°x3° region. It is derived from a Gaussian distribution fitted onto the highest part of the distribution (quantile>0.8) taking advantage on the fact that rainfall creates an asymmetrical SSS distribution towards low values. A RR retrieval algorithm is then developed that combines SMOS ∆S and IR information. In case of IR detects rain, SMOS rain rate, RSMOS is derived from SMOS ∆S. We infer the relationship between RSMOS and SMOS ∆S using colocations within 30mn between SMOS ∆S and RPMW contained in IMERG product during the 2015 year.Correlation coefficient (r) between RSMOS and RPMW is equal to 0.75 (0.78 when the colocation radii is decreased to 3mn). In case there is no RPMW at less than 1h20mn from RSMOS, r is decreased to 0.62. We then compare the RSMOS with the IMERG merged product (RmPMW ). In case there are RPMW at less than 30mn (3mn) from SMOS pass, correlation coefficients remain about the same as previously. In case there is no RPMW at less than 1h20 mn from RSMOS, r between RSMOS and RmPMW becomes equal to 0.72. This demonstrates that the merging of RPMW with IR information by IMERG improves the rain detection with respect to taking into account only RPMW but remains poorer than RPMW measurements. This is confirmed by triple collocations between RSMOS, RIR and RPMW.We then evaluate the quality of the retrieval at monthly time scales from August 2014 to July 2016. Hovmöller diagrams show a very good consistency between IMERG and SMOS monthly rain estimates during this period (correlation of 0.92).The SMOS RR retrieval algorithm is also applied to SMAP SSS measurements from January 2016 to July 2016. SMAP rain estimates (RSMAP ) are compared with RSMOS. At monthly time scales, correlation between RSMAP and RSMOS is 0.96