3 research outputs found
Empirical approach to satellite snow detection
Lumipeitteellä on huomattava vaikutus säähän, ilmastoon, luontoon ja yhteiskuntaan. Pelkästään sääasemilla tehtävät lumihavainnot (lumen syvyys ja maanpinnan laatu) eivät anna kattavaa kuvaa lumen peittävyydestä tai muista lumipeitteen ominaisuuksista.
Sääasemien tuottamia havaintoja voidaan täydentää satelliiteista tehtävillä havainnoilla. Geostationaariset sääsatelliitit tuottavat havaintoja tihein välein, mutta havaintoresoluutio on heikko monilla alueilla, joilla esiintyy kausittaista lunta. Polaariradoilla sääsatelliittien havaintoresoluutio on napa-alueiden läheisyydessä huomattavasti parempi, mutta silloinkaan satelliitit eivät tuota jatkuvaa havaintopeittoa. Tiheimmän havaintoresoluution tuottavat sääsatelliittiradiometrit, jotka toimivat optisilla aallonpituuksilla (näkyvä valo ja infrapuna).
Lumipeitteen kaukokartoitusta satelliiteista vaikeuttavat lumipeitteen oman vaihtelun lisäksi pinnan ominaisuuksien vaihtelu (kasvillisuus, vesistöt, topografia) ja valaistusolojen vaihtelu. Epävarma ja osittain puutteellinen tieto pinnan ja kasvipeitteen ominaisuuksista vaikeuttaa luotettavan automaattisen analyyttisen lumentunnistusmenetelmän kehittämistä ja siksi empiirinen lähestymistapa saattaa olla toimivin vaihtoehto automaattista lumentunnistusmenetelmää kehitettäessä.
Tässä työssä esitellään kaksi EUMETSATin osittain rahoittamassa H SAFissa kehitettyä lumituotetta ja niissä käytetyt empiiristä lähestymistapaa soveltaen kehitetyt algoritmit. Geostationaarinen MSG/SEVIRI H31 lumituote on saatavilla vuodesta 2008 alkaen ja polaarituote Metop/AVHRR H32 vuodesta 2015 alkaen. Lisäksi esitellään pintahavaintoihin perustuvat validointitulokset, jotka osoittavat tuotteiden saavuttavan määritellyt tavoitteet.Snow cover plays a significant role in the weather and climate system, ecosystems and many human activities, such as traffic. Weather station snow observations (snow depth and state of the ground) do not provide highresolution continental or global snow coverage data.
The satellite observations complement in situ observations from weather stations. Geostationary weather satellites provide observations at high temporal resolution, but the spatial resolution is low, especially in polar regions. Polarorbiting weather satellites provide better spatial resolution in polar regions with limited temporal resolution. The best detection resolution is provided by optical and infra-red radiometers onboard weather satellites.
Snow cover in itself is highly variable. Also, the variability of the surface properties (such as vegetation, water bodies, topography) and changing light conditions make satellite snow detection challenging. Much of this variability is in subpixel scales, and this uncertainty creates additional challenges for the development of snow detection methods. Thus, an empirical approach may be the most practical option when developing algorithms for automatic snow detection.
In this work, which is a part of the EUMETSAT-funded H SAF project, two new empirically developed snow extent products for the EUMETSAT weather satellites are presented. The geostationary MSG/SEVIRI H32 snow product has been in operational production since 2008. The polar product Metop/AVHRR H32 is available since 2015. In addition, validation results based on weather station snow observations between 2015 and 2019 are presented. The results show that both products achieve the requirements set by the H SAF
Analysis of the precipitation characteristics on the Tibetan Plateau using Remote Sensing, Ground-Based Instruments and Cloud models
In this Thesis work, carried out in the frame of CEOP-AEGIS, an EU FP7 funded
project, the problem of the precipitation monitoring over the Tibetan Plateau has
been addressed. Despite the Plateau key role in water cycle of South East Asia
(and in the life of 1.5 billions of people), there is a critical lack of knowledge,
because the current estimates of relevant geophysical parameters are based on
sparse and scarce observations than can not provide the required accuracy for
quantitative studies and reliable monitoring, especially on a climate change
perspective. This is particularly true for precipitation, the geophysical parameter
with highest spatial and temporal variability.
The constantly increasing availability of Earth system observation from spaceborne
sensors makes the remote sensing an effective option for precipitation
monitoring and the main focus of the present work is the implementation and
applications for three years of data (2008, 2009 and 2010) of an array of satellite
precipitation techniques, based on different methodological approaches and data
sources.
First, a sensitivity study on the capability of the most used satellite sensors to
detect precipitation at the ground, assessed with respect to raingauges data for
selected case studies, has been carried out.
Then, two physically based techniques have been implemented based on satelliteborne
active (for snow-rate) and passive (for rain-rate) microwave sensor data and
the output used for calibrate geostationary IR-based techniques.
Finally, two well established global multisensor precipitation products have been
considered for reference and intercomparison. All the techniques have been
implemented for the 3 years and the results compared at different spatial and
temporal scales.
The analysis of daily rain amount has shown that in general global algorithms are
able to estimate rain amount larger than the ones estimated by other techniques
during the monsoon season. In cold months global techniques underestimate precipitation amount and areas, resulting in a dry bias with respect to IR calibrated
techniques. Case studies compared with ground radar precipitation data on
convective episodes shown that global products tend to underestimate
precipitation areas, while IR calibrated techniques provides reliable rainrate
patterns, as compared with radar data. Unfortunately, the number of radar case
studies was not large enough to allow significant validation studies, and also non
data were available for cold months.
Annual precipitation cumulated maps show marked differences among the
techniques: IR calibrated techniques generally overestimate precipitation amount
by a factor of 2 with respect of global products.
Reasons for discrepancies are investigated and discussed, pointing out the
uncertainties that will probably be solved only with the exploitation of new
satellite missions