ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. This dataset includes all 50 variables as available on CDS. ERA5-Land data is available from 1950 to three months from real-time. Please consult the ERA5-Land "Known Issues" section. In particular, note that three components of the total evapotranspiration have values swapped as follows: variable "Evaporation from bare soil" (mars parameter code 228101 (evabs)) has the values corresponding to the "Evaporation from vegetation transpiration" (mars parameter 228103 (evavt)), variable "Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the "Evaporation from bare soil" (mars parameter code 228101 (evabs)), variable "Evaporation from vegetation transpiration" (mars parameter code 228103 (evavt)) has the values corresponding to the "Evaporation from open water surfaces excluding oceans" (mars parameter code 228102 (evaow)). The asset is a daily aggregate of ECMWF ERA5 Land hourly assets which includes both flow and non-flow bands. Flow bands are formed by collecting the first hour's data of the following day which holds aggregated sum of previous day and while the non-flow bands are created by averaging all hourly data of the day. The flow bands are labeled with the "_sum" identifier, which approach is different from the daily data produced by Copernicus Climate Data Store, where flow bands are averaged too. Daily aggregates have been pre-calculated to facilitate many applications requiring easy and fast access to the data. Precipitation and other flow (accumulated) bands might occasionally have negative values, which doesn't make physical sense. At other times their values might be excessively high. This problem is due to how the GRIB format saves data: it simplifies or "packs" the data into smaller, less precise numbers, which can introduce errors. These errors get worse when the data varies a lot. Because of this, when we look at the data for a whole day to compute daily totals, sometimes the highest amount of rainfall recorded at one time can seem larger than the total rainfall measured for the entire day. To learn more, Please see: "Why are there sometimes small negative precipitation accumulations"

ERA5-Land to zestaw danych z ponownym przeanalizowaniem, który zapewnia spójny obraz ewolucji zmiennych dotyczących lądu w ciągu kilku dekad w ulepszonej rozdzielczości w porównaniu z ERA5. Dane ERA5-Land zostały wygenerowane przez odtworzenie komponentu lądowego w ramach ponownej analizy klimatu ECMWF ERA5. Reanalysis łączy dane modelu z obserwacjami z całego świata…

Climate reanalysis and climate projection datasets offer the potential for researchers, students and instructors to access physically informed, global scale, temporally and spatially continuous climate data from the latter half of the 20th century to present, and explore different potential future climates. While these data are of significant use to research and teaching within biological, environmental and social sciences, potential users often face barriers to processing and accessing the data that cannot be overcome without specialist knowledge, facilities or assistance. Consequently, climate reanalysis and projection data are currently substantially under-utilised within research and education communities. To address this issue, we present two simple “point-and-click” graphical user interfaces: the Google Earth Engine Climate Tool (GEEClimT), providing access to climate reanalysis data products; and Google Earth Engine CMIP6 Explorer (GEECE), allowing processing and extraction of CMIP6 projection data, including the ability to create custom model ensembles. Together GEEClimT and GEECE provide easy access to over 387 terabytes of data that can be output in commonly used spreadsheet (CSV) or raster (GeoTIFF) formats to aid subsequent offline analysis. Data included in the two tools include: 20 atmospheric, terrestrial and oceanic reanalysis data products; a new dataset of annual resolution climate variables (comparable to WorldClim) calculated from ERA5-Land data for 1950-2022; and CMIP6 climate projection output for 34 model simulations for historical, SSP2-4.5 and SSP5-8.5 scenarios. New data products can also be easily added to the tools as they become available within the Google Earth Engine Data Catalog. Five case studies that use data from both tools are also provided. These show that GEEClimT and GEECE are easily expandable tools that remove multiple barriers to entry that will open use of climate reanalysis and projection data to a new and wider range of users.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

ERA5-Land เป็นชุดข้อมูลการวิเคราะห์ใหม่ซึ่งให้มุมมองที่สอดคล้องกันเกี่ยวกับการเปลี่ยนแปลงของตัวแปรบนบกในช่วงหลายทศวรรษที่ผ่านมาที่ความละเอียดที่ดีขึ้นเมื่อเทียบกับ ERA5 ERA5-Land สร้างขึ้นโดยการเล่นองค์ประกอบภาคพื้นดินของการวิเคราะห์สภาพภูมิอากาศอีกครั้งของ ECMWF ERA5 การวิเคราะห์ใหม่จะรวมข้อมูลโมเดลเข้ากับข้อมูลที่ได้จากการสังเกตการณ์จากทั่วโลกให้เป็นชุดข้อมูลที่สมบูรณ์และสอดคล้องกับข้อมูลทั่วโลกโดยใช้กฎของฟิสิกส์ การวิเคราะห์ใหม่จะสร้างข้อมูลที่ย้อนกลับไปหลายทศวรรษ ซึ่งให้คำอธิบายที่ถูกต้องเกี่ยวกับสภาพภูมิอากาศในอดีต ชุดข้อมูลนี้มีตัวแปรทั้งหมด 50 รายการตามที่ระบุไว้ใน CDS ข้อมูล ERA5-Land มีตั้งแต่ปี 1950 จนถึง 3 เดือนที่ผ่านมา โปรดดูส่วน "ปัญหาที่ทราบ" ของ ERA5-Land โดยเฉพาะอย่างยิ่ง โปรดทราบว่าองค์ประกอบ 3 รายการของการระเหยน้ำทั้งหมดมีค่าสลับกันดังนี้ ตัวแปร "การระเหยจากดินเปล่า" (รหัสพารามิเตอร์ของดาวอังคาร 228101 (evabs)) มีค่าที่สอดคล้องกับ "การระเหยจากการคายระเหยของพืช" (พารามิเตอร์ของดาวอังคาร 228103 (evavt)) ตัวแปร "การระเหยจากพื้นผิวน้ำเปิดที่ไม่รวมมหาสมุทร (รหัสพารามิเตอร์ของดาวอังคาร 228102 (evaow)) มีค่าที่สอดคล้องกับ "การระเหยจากดินเปล่า" (รหัสพารามิเตอร์ของดาวอังคาร 228101 (evabs)) ตัวแปร "การระเหยจากการระเหยของพืช" (รหัสพารามิเตอร์ของดาวอังคาร 228103 (evavt)) มีค่าที่สอดคล้องกับ "การระเหยจากพื้นผิวน้ำเปิดที่ไม่รวมมหาสมุทร" (รหัสพารามิเตอร์ของดาวอังคาร 228102 (evaow)) ชิ้นงานนี้เป็นการรวมข้อมูลรายวันของชิ้นงาน ECMWF ERA5 Land รายชั่วโมง ซึ่งรวมทั้งย่านความถี่ที่มีและไม่มีกระแส กลุ่มการไหลจะสร้างขึ้นโดยการรวบรวมข้อมูลของชั่วโมงแรกของวันถัดไปซึ่งมียอดรวมของวันก่อนหน้า ส่วนกลุ่มที่ไม่ใช่การไหลจะสร้างขึ้นโดยหาค่าเฉลี่ยของข้อมูลรายชั่วโมงทั้งหมดของวัน แถบการไหลจะมีป้ายกำกับด้วยตัวระบุ "_sum" ซึ่งเป็นแนวทางที่แตกต่างจากข้อมูลรายวันที่ผลิตโดย Copernicus Climate Data Store ซึ่งมีการหาค่าเฉลี่ยของแถบการไหลด้วยเช่นกัน ระบบได้คํานวณข้อมูลรวมรายวันไว้ล่วงหน้าเพื่ออำนวยความสะดวกให้กับแอปพลิเคชันต่างๆ ที่ต้องใช้การเข้าถึงข้อมูลอย่างรวดเร็วและง่ายดาย บางครั้งแถบปริมาณน้ำฝนและปริมาณน้ำไหลอื่นๆ (สะสม) อาจมีค่าเป็นลบ ซึ่งไม่สมเหตุสมผล บางครั้งค่าเหล่านี้อาจสูงเกินไป ปัญหานี้เกิดจากวิธีที่รูปแบบ GRIB บันทึกข้อมูล โดยรูปแบบนี้จะลดความซับซ้อนหรือ "แพ็ก" ข้อมูลให้เป็นตัวเลขที่เล็กลงและไม่แม่นยำ ซึ่งอาจทำให้เกิดข้อผิดพลาด ข้อผิดพลาดเหล่านี้จะแย่ลงเมื่อข้อมูลมีความหลากหลายมาก ด้วยเหตุนี้ เมื่อเราดูข้อมูลทั้งวันเพื่อคํานวณยอดรวมรายวัน บางครั้งปริมาณน้ำฝนสูงสุดที่บันทึกไว้ในช่วงเวลาหนึ่งอาจดูมากกว่าปริมาณน้ำฝนทั้งหมดที่วัดได้ตลอดทั้งวัน ดูข้อมูลเพิ่มเติมได้ที่ "ทำไมบางครั้งจึงมีการสะสมปริมาณน้ำฝนเชิงลบเล็กน้อย"

If you use this dataset please cite the accompanying paper (Lea et al., 2024)

Maps of key (bio-)climatic variables derived from all currently available ERA5-Land reanalysis data (Muñoz Sabater et al., 2019). These have been calculated for:

1. All possible World Meteorological Organisation (WMO) 30 year climate baseline periods, including: 1951 to 1980; 1961 to 1990; 1971 to 2000; 1981 to 2010; and 1991 to 2020 (this dataset).

2. Annual timescales from 1951-2022 (see here).

Annual timescale data are calculated using monthly statistics using calendar months that account for leap years. WMO baseline maps are calculated by taking the mean of all annual timescale ERALClim maps that fall within the time periods stated above (inclusive). Image bands are named to map onto equivalent BioClim variables (Fick and Hijmans, 2017).

Global data are provided here in GeoTIFF format as multiband images (where each band represents a different year/variable depending on the data downloaded) at a spatial scale of 0.1 degrees within a WGS84 grid (EPSG:4326). If users require data from point locations and/or subset regions for a specific time range or for a custom range of variables, these can be easily accessed using the Google Earth Engine Climate Tool (GEEClimT). Access to this tool requires a Google Earth Engine account, and is free to use for academic research and education purposes, and users who access data through the tool should cite Lea et al., 2024.

Descriptions of each band within the dataset are listed below:

bio1 - Mean 2 m air temperature derived from hourly data (units: degrees C).

bio2 - Annual mean of monthly mean diurnal 2 m air temperature ranges (units: degrees C).

bio3 - Isothermality (100 * bio2 / bio7) (no units).

bio4 - Standard deviation of monthly mean 2 m air temperatures (units: degrees C).

bio5 - Mean of maximum 2 m air temperature for the warmest month (units: degrees C).

bio6 - Mean of minimum 2 m air temperature for the coldest month (units: degrees C).

bio7 - Annual range of 2 m air temperature (bio5 - bio6) (units: degrees C).

bio8 - Mean 2 m air temperature of wettest 3 month period (units: degrees C).

bio9 - Mean 2 m air temperature of driest 3 month period (units: degrees C).

bio10 - Mean 2 m air temperature of warmest 3 month period (units: degrees C).

bio11 - Mean 2 m air temperature of coldest 3 month period (units: degrees C).

bio12 - Total annual precipitation (units: mm).

bio13 - Total precipitation of wettest month (units: mm).

bio14 - Total precipitation of driest month (units: mm).

bio15 - Precipitation Seasonality (Coefficient of Variation, based on monthly total precipitation data) (no units).

bio16 - Total precipitation in wettest 3 month period (units: mm).

bio17 - Total precipitation in driest 3 month period (units: mm).

bio18 - Total precipitation in warmest 3 month period (units: mm).

bio19 - Total precipitation in coldest 3 month period (units: mm).

ERA5-Land は、ERA5 と比較して解像度が向上した数十年にわたる陸地変数の推移の一貫したビューを提供する再解析データセットです。ERA5-Land は、ECMWF ERA5 気候再解析の陸地コンポーネントを再生して作成されました。再解析では、モデルデータと世界中の観測データを組み合わせて、

ERA5-Land 是一种再分析数据集，与 ERA5 相比，其分辨率更高，可提供几十年来陆地变量演变的一贯视图。ERA5-Land 是通过重放ECMWF ERA5 气候再分析的陆地组件而生成的。重分析会将模型数据与来自世界各地的观测数据相结合，

ERA5-Land là một tập dữ liệu phân tích lại, cung cấp thông tin nhất quán về sự phát triển của các biến trên đất trong vài thập kỷ ở độ phân giải cao hơn so với ERA5. ERA5-Land được tạo bằng cách phát lại thành phần đất của quá trình phân tích lại khí hậu ERA5 của ECMWF. Phân tích lại kết hợp dữ liệu mô hình với dữ liệu quan sát từ khắp nơi trên thế giới thành một tập dữ liệu hoàn chỉnh và nhất quán trên toàn cầu bằng cách sử dụng các định luật vật lý. Phân tích lại tạo ra dữ liệu từ vài thập kỷ trước, cung cấp thông tin mô tả chính xác về khí hậu trong quá khứ. Tập dữ liệu này bao gồm tất cả 50 biến có trên CDS. Dữ liệu được trình bày ở đây là một tập hợp con của tập dữ liệu ERA5-Land đầy đủ do ECMWF xử lý sau. Giá trị trung bình hằng tháng đã được tính toán trước để tạo điều kiện cho nhiều ứng dụng yêu cầu truy cập dễ dàng và nhanh chóng vào dữ liệu, khi không cần các trường hằng tháng phụ. Xin lưu ý rằng quy ước tích luỹ dùng trong ERA5-Land khác với quy ước dùng trong ERA5. Các giá trị tích luỹ được xử lý giống như các giá trị trong ERA-Interim hoặc ERA-Interim/Land, tức là các giá trị này được tích luỹ từ đầu thời gian dự báo đến cuối bước dự báo. Quá trình này diễn ra trong mỗi ngày và được đặt lại vào lúc nửa đêm. Nhóm Dữ liệu Earth Engine đã thêm 19 dải bổ sung, một dải cho mỗi dải tích luỹ, với các giá trị hằng giờ được tính là chênh lệch giữa hai bước dự báo liên tiếp.

ERA5-Land è un set di dati di reanalisi che fornisce una visione coerente dell'evoluzione delle variabili relative alla terra nell'arco di diversi decenni a una risoluzione migliorata rispetto a ERA5. ERA5-Land è stato prodotto riproducendo il componente terrestre della rianalisi climatica ECMWF ERA5. La nuova analisi combina i dati del modello con le osservazioni di tutto il mondo …

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. ERA5 MONTHLY provides aggregated values for each month for seven ERA5 climate reanalysis parameters: 2m air temperature, 2m dewpoint temperature, total precipitation, mean sea level pressure, surface pressure, 10m u-component of wind and 10m v-component of wind. Additionally, monthly minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Monthly total precipitation values are given as monthly sums. All other parameters are provided as monthly averages. ERA5 data is available from 1940 to three months from real-time, the version in the EE Data Catalog is available from 1979. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Monthly aggregates have been calculated based on the ERA5 hourly values of each parameter.

ERA5-Land 是一种再分析数据集，与 ERA5 相比，其分辨率更高，可提供几十年来陆地变量演变的一贯视图。ERA5-Land 是通过重放ECMWF ERA5 气候再分析的陆地组件而生成的。重分析会将模型数据与来自世界各地的观测数据相结合，

ERA5-Land, ERA5'e kıyasla daha yüksek çözünürlükte, karasal değişkenlerin birkaç on yıl içindeki gelişiminin tutarlı bir görünümünü sunan bir yeniden analiz veri kümesidir. ERA5-Land, ECMWF ERA5 iklim yeniden analizinin kara bileşeni yeniden oynatılarak üretilmiştir. Yeniden analiz, model verilerini dünyanın dört bir yanından alınan gözlemlerle birleştirir.

ERA5-Land ist ein Reanalysedatensatz, der einen konsistenten Überblick über die Entwicklung von Landvariablen über mehrere Jahrzehnte hinweg bietet, und zwar mit einer höheren Auflösung als ERA5. ERA5-Land wurde durch die Wiedergabe der Landkomponente der ERA5-Klimareanalyse des ECMWF erstellt. Bei der Reanalyse werden Modelldaten mit Beobachtungen aus der ganzen Welt kombiniert…

ERA5-Land 是一种再分析数据集，与 ERA5 相比，其分辨率更高，可提供几十年来陆地变量演变的一贯视图。ERA5-Land 是通过重放ECMWF ERA5 气候再分析的陆地组件而生成的。重分析会将模型数据与来自世界各地的观测数据相结合，

ERA5-Land là một tập dữ liệu phân tích lại, cung cấp thông tin nhất quán về sự phát triển của các biến trên đất trong vài thập kỷ ở độ phân giải nâng cao so với ERA5. ERA5-Land được tạo bằng cách phát lại thành phần đất của quá trình phân tích lại khí hậu ERA5 của ECMWF. Phân tích lại kết hợp dữ liệu mô hình với dữ liệu quan sát từ khắp nơi trên thế giới thành một tập dữ liệu hoàn chỉnh và nhất quán trên toàn cầu bằng cách sử dụng các định luật vật lý. Phân tích lại tạo ra dữ liệu từ vài thập kỷ trước, cung cấp thông tin mô tả chính xác về khí hậu trong quá khứ. Tập dữ liệu này bao gồm tất cả 50 biến có trên CDS. Dữ liệu ERA5-Land có sẵn từ năm 1950 đến 3 tháng trước thời gian thực. Vui lòng tham khảo phần "Các vấn đề đã biết" của ERA5-Land. Cụ thể, hãy lưu ý rằng ba thành phần của tổng lượng bốc hơi có giá trị được hoán đổi như sau: biến "Hấp thụ từ đất trống" (mã tham số sao Hoả 228101 (evabs)) có các giá trị tương ứng với "Hấp thụ từ quá trình thoát hơi của thảm thực vật" (mã tham số sao Hoả 228103 (evavt)), biến "Hấp thụ từ bề mặt nước mở không bao gồm đại dương (mã tham số sao Hoả 228102 (evaow)) có các giá trị tương ứng với "Hấp thụ từ đất trống" (mã tham số sao Hoả 228101 (evabs)), biến "Hơi nước bốc hơi từ quá trình thoát hơi của thực vật" (mã tham số sao Hoả 228103 (evavt)) có các giá trị tương ứng với "Hơi nước bốc hơi từ các bề mặt nước mở không bao gồm đại dương" (mã tham số sao Hoả 228102 (evaow)). Xin lưu ý rằng quy ước tích luỹ được dùng trong ERA5-Land khác với quy ước tích luỹ được dùng trong ERA5. Các giá trị tích luỹ được xử lý giống như các giá trị trong ERA-Interim hoặc ERA-Interim/Land, tức là các giá trị này được tích luỹ từ đầu thông tin dự báo đến cuối bước dự báo. Quá trình này diễn ra trong mỗi ngày và được đặt lại vào lúc nửa đêm. Nhóm Dữ liệu Earth Engine đã thêm 19 dải khác, mỗi dải cho một dải tích luỹ, với các giá trị hằng giờ được tính là chênh lệch giữa hai bước dự báo liên tiếp.

ERA5-Land は、ERA5 と比較して解像度が向上した数十年にわたる陸地変数の推移の一貫したビューを提供する再解析データセットです。ERA5-Land は、ECMWF ERA5 気候再解析の陸地コンポーネントを再生して作成されました。再解析では、モデルデータと世界中の観測データを組み合わせて、

ERA5-Land, फिर से विश्लेषण किया गया डेटासेट है. इसमें ERA5 की तुलना में बेहतर रिज़ॉल्यूशन में, कई दशकों से लैंड वैरिएबल में हुए बदलावों को लगातार देखा जा सकता है. ERA5-Land को, ईसीएमडब्ल्यूएफ़ के ERA5 क्लाइमेट रीऐनालिसिस के लैंड कॉम्पोनेंट को फिर से चलाकर बनाया गया है. फिर से विश्लेषण करने की सुविधा, मॉडल के डेटा को दुनिया भर से मिली जानकारी के साथ जोड़ती है …