Editing IPCC:AR6/WGI/Chapter-10 (section)

=== 10.2.1 Observation Types and Their Use at Regional Scale ===

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==== 10.2.1.1 In Situ and Remote-sensing Data ====

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Surface or in situ observations can come from a variety of networks: climate reference networks, mesoscale weather and supersite observation networks, citizen science networks, among others, all with their strengths and weaknesses ( [[#McPherson--2013|McPherson, 2013]] ; [[#Thorne--2018|Thorne et al., 2018]] ). Supersite observatories are surface and atmospheric boundary layer observing networks that measure a large number of atmospheric and soil variables at least hourly over a decade or more, ideally located in rural areas ( [[#Ackerman--2003|Ackerman and Stokes, 2003]] ; [[#Haeffelin--2005|Haeffelin et al., 2005]] ; [[#Xie--2010|Xie et al., 2010]] ; [[#Chiriaco--2018|Chiriaco et al., 2018]] ). Adequate calibration of instruments, quality control and homogenization are essential in these sites. They produce valuable data needed to diagnose processes and changes in regional and local climate. Many climate datasets have been developed from in situ station observations, at different spatial scales and temporal frequencies (Annex I: Observational Products). These include sub-daily ( [[#Dumitrescu--2016|Dumitrescu et al., 2016]] ; [[#Blenkinsop--2017|Blenkinsop et al., 2017]] ), daily ( [[#Chen--2008|Chen et al., 2008]] ; Camera et al., 2014; [[#Journée--2015|Journée et al., 2015]] ; [[#Funk--2015|Funk et al., 2015]] ; [[#Aalto--2016|Aalto et al., 2016]] ; [[#Beck--2017a|Beck et al., 2017a]] , b; [[#Schneider--2017|Schneider et al., 2017]] ) or monthly time scales ( [[#Cuervo-Robayo--2014|Cuervo-Robayo et al., 2014]] ; [[#Aryee--2018|Aryee et al., 2018]] ). Sub-daily data is useful for estimating storm surge ( [[#Mori--2014|Mori et al., 2014]] ) or river discharge ( [[#Shrestha--2015|Shrestha et al., 2015]] ), daily data for carbon-stock dynamics ( [[#Haga--2020|Haga et al., 2020]] ) or tourism ( [[#Watanabe--2018|Watanabe et al., 2018]] ), and monthly data for beach morphology ( [[#Bennett--2019|Bennett et al., 2019]] ).

Satellite products provide a valuable complement to in situ measurements, particularly over regions where in situ measurements are unavailable. They have been discussed in earlier chapters (e.g., Chapters 2 and 8) for large-scale assessment. Currently 54 essential climate variables (ECVs; [[#Bojinski--2014|Bojinski et al., 2014]] ) are defined by the Global Climate Observing System (GCOS) program, and passed on, for example, to NASA programmes through the Decadal Survey, to the Copernicus Climate Change Service of the European Union, to the ESA Climate Change Initiative ESA-CCI, as well as to the international collaborations with geostationary Earth orbit (GEO) satellites. Their observations are valuable ( ''high confidence'' ) for regional applications since they provide multi-channel images at very high spatiotemporal resolutions, typically 16 channels, 1–2 km, every 10 to 15 minutes. The advanced geostationary satellites are: Himawari-8 and 9 ( [[#Kurihara--2016|Kurihara et al., 2016]] ), GOES-East and GOES-17 ( [[#Goodman--2018|Goodman et al., 2018]] ), Meteosat-10 and 11 ( [[#Schmetz--2002|Schmetz et al., 2002]] ) and FY-4 ( [[#Cao--2014|Cao et al., 2014]] ). Geostationary satellite networks or constellations form an essential component of the Global Observation System ( https://www.wmo.int/pages/prog/www/OSY/GOS.html ), providing measurements not only for various cloud properties and moisture but also for air quality, land and ocean surface conditions, and lightning.

Low Earth orbit (LEO) satellites, with orbits typically at 400–700 km, provide advanced measurements of the Earth’s surface. Sun-synchronous polar orbiters can also cover the polar regions, which cannot be observed with GEO satellites. Examples of LEO observations for land surface monitoring are NASA’s Landsat ( [[#Wulder--2016|Wulder et al., 2016]] ), ESA’s Soil Moisture Ocean Salinity Earth Explorer (SMOS) mission ( [[#Kerr--2012|Kerr et al., 2012]] ), the Sentinel missions of the Copernicus programme, and JAXA’s ALOS-2 ( [[#Ohki--2019|Ohki et al., 2019]] ), providing high spatial resolution land surface images. Many kinds of data are accumulated for land use and land cover studies, targeting aspects like urban footprint ( [[#Florczyk--2019|Florczyk et al., 2019]] ), land-cover data (Global Land 30; CCI-LC: [[#ESA--2021|ESA, 2021]] ; [[#Chen--2018|Chen and Chen, 2018]] ), land surfacetemperature data (Landsat, [[#Parastatidis--2017|Parastatidis et al., 2017]] ), and surface albedo ( [[#Chrysoulakis--2019|Chrysoulakis et al., 2019]] ).

Availability of active sensors on LEO satellites enables measurement of microphysical properties of aerosol, cloud and precipitation, which can advance regional climate studies and process evaluation studies to improve regional climate models ( ''high confidence'' ). An example is the polar-orbiting ‘afternoon-train’ satellite constellation (known as the A-train), incorporating Aqua, CALIPSO, Cloudsat, PARASOL, Glory and Aura satellites. Vertical profiling observations from Cloudsat (with a W-band cloud radar) and CALIPSO (with a cloud lidar) led to considerable advances in measurements of cloud microphysics ( [[#Stephens--2018|Stephens et al., 2018]] ). Precipitation and its extremes are essential concerns of regional climate studies. The GPM (65°N–65°S, 2014–present) and the preceding TRMM (36.5°N–36.5°S, 1997–2015) with Ku-/Ka-band precipitation radars have provided three-dimensional measurements of precipitation with about 5 km resolution and sub-daily sampling ( [[#Skofronick-Jackson--2017|Skofronick-Jackson et al., 2017]] ). Their non-sun-synchronous observation works to cross-calibrate the constellation satellites to produce global high-resolution mapped products of precipitation, such as Integrated Multi-satellitE Retrievals for GPM (IMERG; [[#Huffman--2007|Huffman et al., 2007]] ) and the Global Satellite Mapping of Precipitation (GSMaP; [[#Kubota--2007|Kubota et al., 2007]] ), with hourly sampling at about 11 km resolution. The CPC MORPHing technique (CMORPH) has provided 30 min interval global precipitation with about 8 km coverage since 2002 ( [[#Joyce--2004|Joyce et al., 2004]] ). Precipitation estimations from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is a sub-daily to daily rainfall product that covers 50°S to 50°N globally with 25 km resolution from 2000 to the present ( [[#Nguyen--2019|Nguyen et al., 2019]] ), and is used for semi-global-scale precipitation coverage ( [[#Benestad--2018|Benestad, 2018]] ). TRMM/GPM observations have enabled estimates to be obtained for global four-dimensional convective heating ( [[#Shige--2009|Shige et al., 2009]] ; [[#Tao--2016|Tao et al., 2016]] ; [[#Takayabu--2020|Takayabu and Tao, 2020]] ).

The use of these data has enhanced our understanding of precipitation processes at regional scale ( ''high confidence'' ), such as diurnal cycles in a large river valley (H. [[#Chen--2012|]] [[#Chen--2012|Chen et al., 2012]] ), and in coastal ( [[#Hassim--2016|Hassim et al., 2016]] ; [[#Yokoi--2017|Yokoi et al., 2017]] ) and mountainous regions ( [[#Hirose--2017|Hirose et al., 2017]] ). Three-dimensional observations revealed the contrasts in regional characteristics of rainfall extremes in monsoon regions and continental dry regions ( [[#Sohn--2013|Sohn et al., 2013]] ; [[#Hamada--2018|Hamada and Takayabu, 2018]] ). Satellite measurements are also used to evaluate climate model performance, as well as to develop new parametrizations. As a demonstration of the utility of these products in studying model bias, a subtropical cumulus congestus regime has been identified that may be implicated in the unrealistic double Inter-tropical Convergence Zone (ITCZ) found in some climate models ( [[#Takayabu--2010|Takayabu et al., 2010]] ; [[#Hirota--2011|Hirota et al., 2011]] , 2014). Another example is a parametrization of a land surface model that was developed specifically for a certain soil type. By assimilating satellite brightness temperature observations with their LDAS-UT scheme, [[#Yang--2007|Yang et al. (2007)]] successfully optimized a land surface model for the Tibetan Plateau.

For application at a regional scale, it is important to consider variations in the spatiotemporal resolution of the satellite products. A simple concatenation of data in time can show artificial jumps that are artefacts of changes in calibration and processing algorithms, or related to satellite orbital stability or changing performance of the instruments ( [[#Wielicki--2013|Wielicki et al., 2013]] ; [[#Barrett--2014|Barrett et al., 2014]] ). Recalibration and cross-calibration are then prerequisites for obtaining homogeneous time series of measurements across different or successive satellites that can then be used to produce long series that are valid as climate data records ( [[#Kanemaru--2017|Kanemaru et al., 2017]] ; [[#Merchant--2017|Merchant et al., 2017]] ). Scale representativeness is also an issue in utilizing soil observations ( [[#Taylor--2012|Taylor et al., 2012]] , 2013). Although a variety of technologies to measure soil moisture at the point scale exist ( [[#Dobriyal--2012|Dobriyal et al., 2012]] ), its spatial representativeness is less than 1 m <sup>2</sup> ( [[#Ochsner--2013|Ochsner et al., 2013]] ; L. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ). Therefore, to be able to use in situ soil moisture for validating coarser-scale data from satellites or models, networks of point-scale measurements are used ( [[#Crow--2015|Crow et al., 2015]] ; [[#Polcher--2016|Polcher et al., 2016]] ). Smaller networks are typically of the size of a single climate model gridcell or a satellite pixel and are suitable for monitoring watersheds, while small numbers of those representing larger areas (&gt;100 km <sup>2</sup> ) are emerging ( [[#Ochsner--2013|Ochsner et al., 2013]] ).

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==== 10.2.1.2 Derived Products ====

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Derived observational products are created from raw datasets collected from surface stations, remote-sensing instruments, or research vessels, which are converted into meaningful physical quantities by applying a suitable measurement theory, using either statistical interpolation techniques ( [[#10.2.2.4|Section 10.2.2.4]] ) or numerical atmospheric and land surface models ( [[#Bosilovich--2015|Bosilovich et al., 2015]] ).

Most global observational datasets are available at coarse temporal and spatial resolution, and do not include all available station data from a particular region, due to data availability problems. Therefore, efforts have been made to develop regional or country-scale datasets (Annex I). Radar and satellite remote sensing are resources that can provide a valuable complement to direct measurements at regional scale. Examples for precipitation have been described already, some of which have been released to the community ( [[#Dinku--2014|Dinku et al., 2014]] ; [[#Oyler--2015|Oyler et al., 2015]] ; [[#Manz--2016|Manz et al., 2016]] ; [[#Dietzsch--2017|Dietzsch et al., 2017]] ; [[#Yang--2017|Yang et al., 2017]] ; [[#Bližňák--2018|Bližňák et al., 2018]] ; [[#Krähenmann--2018|Krähenmann et al., 2018]] ; [[#Panziera--2018|Panziera et al., 2018]] ; [[#Shen--2018|Shen et al., 2018]] ). However, some of these datasets are limited by their short record, varying between one ( [[#Shen--2018|Shen et al., 2018]] ) and 64 years ( [[#Oyler--2015|Oyler et al., 2015]] ).

Reanalysis products are numerical climate simulations that use data assimilation to incorporate as many irregular observations as possible. These products encompass many physical and dynamical processes. They generate a coherent estimate of the state of the climate system on uniform grids either at global ( [[#Chaudhuri--2013|Chaudhuri et al., 2013]] ; [[#Balsamo--2015|Balsamo et al., 2015]] ), regional ( [[#Chaney--2014|Chaney et al., 2014]] ; [[#Maidment--2014|Maidment et al., 2014]] ; [[#Dahlgren--2016|Dahlgren et al., 2016]] ; [[#Langodan--2017|Langodan et al., 2017]] ; [[#Attada--2018|Attada et al., 2018]] ; [[#Mahmood--2018|Mahmood et al., 2018]] ) or country scales ( [[#Rostkier-Edelstein--2014|Rostkier-Edelstein et al., 2014]] ; Krähenmannet al., 2018; [[#Mahmood--2018|Mahmood et al., 2018]] ).

Reanalyses incorporate an increasing volume of observations from a growing number of sources over time, which sometimes presents a difficulty for trend analysis. However, regional reanalyses are valuable for regional climate assessments, since they can employ high-resolution model simulations due to their limited spatial domain. Their accuracy is also better than global reanalyses since they are often developed over regions with a high density of observational data (sometimes not freely available for all regions) to be assimilated into the model (e.g., [[#Yamada--2012|Yamada et al., 2012]] ). Regional reanalyses can assimilate locally dense and high-frequency observations, such as from local observation networks ( [[#Mahmood--2018|Mahmood et al., 2018]] ; [[#Su--2019|Su et al., 2019]] ) and radar precipitation ( [[#Wahl--2017|Wahl et al., 2017]] ) in addition to the observations assimilated by global reanalyses. In some regional reanalyses, satellite-derived high-resolution sea ice ( [[#Bromwich--2016|Bromwich et al., 2016]] , 2018) and sea surface temperature ( [[#Su--2019|Su et al., 2019]] ) are also applied as lower boundary conditions. The periods of regional reanalyses are limited by the availability of the observations for assimilation and by the global reanalyses needed as lateral boundary conditions. Most regional reanalyses cover the past 10 to 30 years. There are also regional reanalysis activities that use conventional observations only, which produce consistent datasets over 60 years to capture precipitation trends, extremes and changes ( [[#Fukui--2018|Fukui et al., 2018]] ). Existing regional reanalyses cover North America ( [[#Mesinger--2006|Mesinger et al., 2006]] ), Europe ( [[#Dahlgren--2016|Dahlgren et al., 2016]] ; [[#Jermey--2016|Jermey and Renshaw, 2016]] ; [[#Kaspar--2020|Kaspar et al., 2020]] ), the Arctic ( [[#Bromwich--2016|Bromwich et al., 2016]] , 2018), South Asia ( [[#Mahmood--2018|Mahmood et al., 2018]] ), and Australia ( [[#Su--2019|Su et al., 2019]] ). A project for regional reanalysis covering Japan has also started ( [[#Fukui--2018|Fukui et al., 2018]] ), where grid spacing is between 5 and 32 km, although cumulus parametrizations are still needed to compute sub-grid scale cumulus convection. Recently, reanalyses using convection-permitting regional models have been published (e.g., [[#Wahl--2017|Wahl et al., 2017]] , for central Europe).

The data assimilation schemes used in regional reanalyses are often relatively simple methods, specifically nudging ( [[#Kaspar--2020|Kaspar et al., 2020]] ) and 3DVAR ( [[#Mesinger--2006|Mesinger et al., 2006]] ; [[#Bromwich--2016|Bromwich et al., 2016]] ; [[#Dahlgren--2016|Dahlgren et al., 2016]] ), rather than the more complex schemes implemented in state-of-the-art global reanalysis systems. This is partly due to limitations of computational resources. Recently, a number of regional reanalyses using more sophisticated methods, such as 4DVAR and Ensemble Kalman filter, have been published ( [[#Jermey--2016|Jermey and Renshaw, 2016]] ; [[#Fukui--2018|Fukui et al., 2018]] ; [[#Mahmood--2018|Mahmood et al., 2018]] ; [[#Su--2019|Su et al., 2019]] ). The regional reanalyses also incorporate uncertainties due to deficiencies of the models, data assimilation schemes and observations. To estimate uncertainties, some regional reanalyses apply data assimilation using ensemble forecasts ( [[#Bach--2016|Bach et al., 2016]] ). Another approach compares multiple regional reanalyses produced with different systems covering the same domain, which represents the uncertainties better than single reanalysis systems with ensemble data assimilation schemes ( [[#Kaiser-Weiss--2019|Kaiser-Weiss et al., 2019]] ).

The regional reanalyses represent the frequencies of extremes and the distributions of precipitation, surface air temperature, and surface wind better than global reanalyses ( ''high confidence'' ). This is due to the use of high-resolution regional climate models (RCMs), as indicated by different regional climate modelling studies ( [[#Mesinger--2006|Mesinger et al., 2006]] ; [[#Bollmeyer--2015|Bollmeyer et al., 2015]] ; [[#Bromwich--2016|Bromwich et al., 2016]] , 2018; [[#Dahlgren--2016|Dahlgren et al., 2016]] ; [[#Jermey--2016|Jermey and Renshaw, 2016]] ; [[#Fukui--2018|Fukui et al., 2018]] ; [[#Su--2019|Su et al., 2019]] ). Regional reanalyses, however, retain uncertainties due to deficiencies in the physical parametrization used in RCMs and by the use of relatively simple data assimilation algorithms ( [[#Bromwich--2016|Bromwich et al., 2016]] ; [[#Jermey--2016|Jermey and Renshaw, 2016]] ; [[#Su--2019|Su et al., 2019]] ). Regional reanalyses can provide estimates that are more consistent with observations than dynamical downscaling approaches, due to the assimilation of additional local observations ( ''high confidence'' ) ( [[#Bollmeyer--2015|Bollmeyer et al., 2015]] ; [[#Fukui--2018|Fukui et al., 2018]] ).

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